WO2024244659A1 - 云图处理方法、装置、计算机设备、计算机可读存储介质及计算机程序产品 - Google Patents

云图处理方法、装置、计算机设备、计算机可读存储介质及计算机程序产品 Download PDF

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Publication number
WO2024244659A1
WO2024244659A1 PCT/CN2024/084465 CN2024084465W WO2024244659A1 WO 2024244659 A1 WO2024244659 A1 WO 2024244659A1 CN 2024084465 W CN2024084465 W CN 2024084465W WO 2024244659 A1 WO2024244659 A1 WO 2024244659A1
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Prior art keywords
cloud
sampling
point
coordinate system
information
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PCT/CN2024/084465
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English (en)
French (fr)
Inventor
张智宇
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Tencent Technology (Shenzhen) Co Ltd
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Tencent Technology (Shenzhen) Co Ltd
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Priority to EP24813865.3A priority Critical patent/EP4645235A4/en
Publication of WO2024244659A1 publication Critical patent/WO2024244659A1/zh
Priority to US19/266,151 priority patent/US20250342558A1/en
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/08—Projecting images onto non-planar surfaces, e.g. geodetic screens
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—Three-dimensional [3D] image rendering
    • G06T15/04—Texture mapping
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—Three-dimensional [3D] image rendering
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—Three-dimensional [3D] image rendering
    • G06T15/005—General purpose rendering architectures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—Three-dimensional [3D] image rendering
    • G06T15/10—Geometric effects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three-dimensional [3D] modelling for computer graphics
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three-dimensional [3D] modelling for computer graphics
    • G06T17/05—Geographic models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating three-dimensional [3D] models or images for computer graphics
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/06—Topological mapping of higher dimensional structures onto lower dimensional surfaces
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/06—Topological mapping of higher dimensional structures onto lower dimensional surfaces
    • G06T3/067—Reshaping or unfolding three-dimensional [3D] tree structures onto two-dimensional [2D] planes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present application relates to the field of computer technology, and in particular to a cloud image processing method, device, computer equipment, computer-readable storage medium and computer program product.
  • cloud maps are mainly for flat surfaces.
  • a flat map is generated and applied to the surface of a sphere to generate a spherical surface map.
  • an infinitely large plane map is generally generated manually or programmatically.
  • the longitude and latitude of the location are projected to the corresponding plane coordinates on the plane map, and the plane coordinates are used to sample on the infinitely large plane cloud map.
  • This method is difficult to ensure that the plane coordinates are evenly distributed, and distortion will occur in high-latitude areas, resulting in low accuracy of the longitude and latitude projection results.
  • a whole spherical surface map is directly generated through a modeling tool.
  • the embodiments of the present application provide a cloud image processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product, which can improve the accuracy and efficiency of cloud image processing.
  • the present application provides a cloud image processing method, which includes:
  • the basic cloud map data is sampled to obtain the sampling cloud information of the first sampling point.
  • the present application provides a cloud image processing device, which includes:
  • a cloud map construction module is configured to construct a spatial coordinate system and construct basic cloud map data in the spatial coordinate system
  • a local construction module is configured to construct a local coordinate system corresponding to the first viewpoint position with the first viewpoint position as the origin; different viewpoint positions correspond to different local coordinate systems;
  • a position determination module configured to obtain sampling point position information of the first sampling point in a local coordinate system corresponding to the first viewpoint position, and perform position conversion on the sampling point position information to obtain cloud sampling position information
  • the cloud sampling module is configured to sample the basic cloud map data based on the cloud sampling position information to obtain the sampling cloud information of the first sampling point.
  • the embodiment of the present application provides a computer device, including a processor, a memory, and an input and output interface;
  • the processor is connected to the memory and the input and output interface respectively, wherein the input and output interface is used to receive and output data, the memory is used to store a computer program, and the processor is used to call the computer program so that a computer device including the processor executes the cloud map processing method provided in an embodiment of the present application.
  • the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program.
  • the computer program is suitable for being loaded and executed by a processor so that a computer device having the processor executes the cloud image processing method provided in an embodiment of the present application.
  • the embodiment of the present application provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium.
  • the processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the cloud image processing method provided in the embodiment of the present application.
  • a spatial coordinate system can be constructed, and basic cloud map data can be constructed in the spatial coordinate system; the local coordinate system corresponding to the first viewpoint position is constructed with the first viewpoint position as the origin, the sampling point position information of the first sampling point in the local coordinate system corresponding to the first viewpoint position is obtained, and the sampling point position information is converted to obtain cloud sampling position information; different viewpoint positions correspond to different local coordinate systems; based on the cloud sampling position information, the basic cloud map data is sampled to obtain the sampling cloud information of the first sampling point.
  • a basic cloud map data can be constructed, and then when sampling, the coordinates are determined based on the local coordinate system, that is, it is equivalent to converting the problem of "generating a spherical map on the surface of a sphere” into the problem of "rolling the sphere on a plane and generating a plane map near the tangent point (i.e., sampling of the local coordinate system)", which simplifies the production process and efficiency of cloud map data.
  • based on local sampling when generating cloud map data with higher precision, it is not necessary to consume more overhead and resources, etc., thereby saving resources and improving the accuracy and efficiency of cloud map processing.
  • FIG1 is a network interaction architecture diagram of a cloud image processing provided by an embodiment of the present application.
  • FIG2 is a schematic diagram of a cloud image processing scenario provided in an embodiment of the present application.
  • FIG3 is a flow chart of a cloud image processing method provided in an embodiment of the present application.
  • FIG4a is a schematic diagram of a cloud map construction scenario provided in an embodiment of the present application.
  • FIG4b is an example of cloud map construction provided in an embodiment of the present application.
  • FIG5 is a schematic diagram of another cloud map construction scenario provided in an embodiment of the present application.
  • FIG6 is a schematic diagram of a local coordinate construction scene provided in an embodiment of the present application.
  • FIG7 is a schematic diagram of a coordinate system conversion scenario provided in an embodiment of the present application.
  • FIG8 is a schematic diagram of a location determination scenario provided by an embodiment of the present application.
  • FIG9 is a schematic diagram of a cloud map data iterative processing flow provided in an embodiment of the present application.
  • FIG10 is a schematic diagram of a cloud sampling process provided in an embodiment of the present application.
  • FIG11 is a schematic diagram of a cloud image processing device provided in an embodiment of the present application.
  • FIG. 12 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application.
  • a prompt interface or pop-up window will be displayed before or during the collection.
  • the prompt interface or pop-up window is used to prompt the user that XXXX data is currently being collected. Only after the user confirms the prompt interface or pop-up window, the relevant steps of data acquisition will be started, otherwise it will end.
  • the acquired user data will be used in reasonable and legal scenarios or purposes. In some scenarios where user data needs to be used but the user's authorization is not obtained, authorization can be requested from the user, and the user data can be used after the authorization is passed.
  • the use of user data complies with the relevant provisions of laws and regulations.
  • FIG. 1 is a network interaction architecture diagram of a cloud map processing provided by the embodiment of the present application.
  • the computer device 101 can construct a local coordinate system based on the positions of each viewpoint on the surface of the object model, and determine the cloud information of each sampling point with the local coordinate system, thereby realizing the cloud map parsing of the object model.
  • the computer device 101 can receive a cloud map data rendering request from any one or more business devices, determine the cloud information of each sampling point based on the cloud map data rendering request, and send the determined cloud information to the business device corresponding to the cloud map data rendering request.
  • the number of the business devices can be one or more, such as the business device 102a, business device 102b and business device 102c shown in FIG. 1.
  • the object model refers to the model of the object to be parsed for cloud map parsing
  • the object to be parsed refers to the object for cloud map parsing, which can be a physical object or a virtual object in an application, etc.
  • the physical object can be but not limited to the earth or other objects with larger surfaces
  • the virtual object can be but not limited to objects that need to be processed by cloud map in the application, such as virtual planets in game applications, etc.
  • each sampling point can be analyzed based on the local coordinate system, so that the problem of "generating a spherical map on the surface of a sphere" can be simplified to the problem of "local mapping”, which simplifies the cloud map processing process and improves the accuracy and efficiency of cloud map processing.
  • the computer device can construct a spatial coordinate system 201, which includes a horizontal coordinate axis (u-axis), a vertical coordinate axis (v-axis) and a coordinate system origin (ori point).
  • the spatial coordinate system 201 can be considered as a plane coordinate system.
  • the horizontal coordinate axis and the vertical coordinate axis can be constructed with two cloud distribution ranges (such as 0 to 1, etc.), and the spatial coordinate system 201 is constructed based on the horizontal coordinate axis and the vertical coordinate axis.
  • basic cloud map data 202 can be constructed in the spatial coordinate system 201.
  • the cloud map data (Cloud-map/Weather-map/Weather-texture) in the embodiment of the present application is used to represent the distribution and morphology of volumetric clouds from a bird's-eye view, that is, a cloud map.
  • the first viewpoint position 203 can be used as the origin to construct a local coordinate system 204 corresponding to the first viewpoint position 203.
  • the local coordinate system 204 can be considered to be generated based on the tangent plane of the object model at the first viewpoint position 203.
  • the sampling point position information of the first sampling point 205 in the local coordinate system 204 corresponding to the first viewpoint position 203 can be obtained, and the sampling point position information is converted to obtain cloud sampling position information.
  • the cloud sampling position information is used to represent the position of the first sampling point 205 in the basic cloud image data 202.
  • Different viewpoint positions correspond to different local coordinate systems.
  • the basic cloud image data 202 can be sampled to obtain the sampling cloud information of the first sampling point 205.
  • each sampling point determines the cloud sampling position information in the local coordinate system corresponding to the adjacent viewpoint position.
  • the construction of the local coordinate system makes the area corresponding to each local coordinate system smaller, and the plane coordinates under each local coordinate system are distributed more evenly, so that each sampling point can be better and more accurately mapped to the basic cloud map data for sampling, thereby improving the accuracy and efficiency of cloud map processing.
  • the computer device mentioned in the embodiment of the present application includes but is not limited to a terminal device or a server.
  • the computer device can be a server or a terminal device, or a system composed of a server and a terminal device.
  • the terminal device mentioned above can be an electronic device, including but not limited to a mobile phone, a tablet computer, a desktop computer, a laptop computer, a PDA, a vehicle-mounted device, an augmented reality/virtual reality (AR/VR) device, a helmet display, a smart TV, a wearable device, a smart speaker, a digital camera, a camera and other mobile Internet devices (mobile internet device, MID) with network access capability, or a terminal device in a scene such as a train, a ship, or a flight.
  • a mobile internet device MID
  • the terminal device can be a laptop computer (as shown in business device 102b), a mobile phone (as shown in business device 102c) or a vehicle-mounted device (as shown in business device 102a), etc.
  • Figure 1 only exemplifies some of the devices.
  • the business device 102a refers to a device located in a vehicle 103, and the business device 102a can be used to display cloud map data, etc.
  • the servers mentioned above can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, vehicle-road collaboration, content delivery networks (CDN), and big data and artificial intelligence platforms.
  • Cloud server for services can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, vehicle-road collaboration, content delivery networks (CDN), and big data and artificial intelligence platforms.
  • Cloud server for services can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, vehicle-road collaboration, content delivery networks (CDN), and big data and artificial intelligence platforms.
  • Cloud server for services can
  • the data involved in the embodiments of the present application can be stored in a computer device, or the data can be stored based on cloud storage technology or a blockchain network, without limitation here.
  • FIG 3 is a flow chart of a cloud graph processing method provided by an embodiment of the present application. As shown in Figure 3, the cloud graph processing process includes the following steps:
  • Step S301 constructing a spatial coordinate system, and constructing basic cloud map data in the spatial coordinate system.
  • the computer device can implement the construction of basic cloud map data in the spatial coordinate system in step S301 in the following manner: obtain candidate cloud map data, and associate the candidate cloud map data with the spatial coordinate system to generate initial unit cloud map data; determine the initial unit cloud map data as basic cloud map data, or seamlessly continue the initial unit cloud map data to generate basic cloud map data.
  • the computer device may first construct a spatial coordinate system, construct initial unit cloud map data in the spatial coordinate system, and determine the initial unit cloud map data as basic cloud map data; or, based on the initial unit cloud map data, seamlessly continue the initial unit cloud map data, including seamless connection up and down and left and right, etc., to generate basic cloud map data.
  • a spatial coordinate system may be constructed, the spatial coordinate system constructs the horizontal coordinate with the first value range, constructs the vertical coordinate with the second value range, and the horizontal coordinate and the vertical coordinate form a spatial coordinate system; obtain candidate cloud map data, associate the candidate cloud map data with the spatial coordinate system, and generate initial unit cloud map data.
  • associating the candidate cloud map data with the spatial coordinate system refers to determining the position of each pixel point in the candidate cloud map data in the spatial coordinate system.
  • the candidate cloud map data refers to fine cloud map data drawn manually, or fine cloud map data generated by a model and optimized manually, etc.
  • the size of the candidate cloud map data is small.
  • the size of the candidate cloud map data is less than or equal to the unit size threshold, such as 50 meters ⁇ 50 meters, etc. It can be specifically determined based on the cost of drawing the cloud map data.
  • the unit size threshold can be increased, etc., so that when generating the candidate cloud map data, only less labor cost and resources are required to obtain the required cloud map data, thereby reducing the cost of generating cloud map data.
  • the computer device can implement step S301 in the following manner: obtain the image to be analyzed, perform coordinate conversion on the image size of the image to be analyzed, and obtain a spatial coordinate system; perform image analysis on the image to be analyzed, map the analysis result to the spatial coordinate system, and obtain initial unit cloud map data; based on the initial unit cloud map data, seamlessly continue the initial unit cloud map data to generate basic cloud map data.
  • a computer device can obtain an image to be analyzed, perform coordinate conversion on the image size of the image to be analyzed, and obtain a spatial coordinate system.
  • Figure 4a is a schematic diagram of a cloud map construction scene provided in an embodiment of the present application.
  • the computer device can use the lower left corner 4011 of the image to be analyzed 401 as the origin of the coordinate system, construct a horizontal coordinate axis (u axis) based on the width of the image to be analyzed 401, and construct a vertical coordinate axis (v axis) based on the height of the image to be analyzed 401.
  • the origin of the coordinate system, the horizontal coordinate axis and the vertical coordinate axis construct an initial coordinate system, which can be considered as a two-dimensional coordinate system.
  • the horizontal coordinate value in the two-dimensional coordinate system belongs to the first value range, and the vertical coordinate value belongs to the second value range, wherein the first value range and the second value range can be considered as the default texture coordinate range, that is, the UV coordinate range "0 ⁇ 1" and the like;
  • the size of the image to be analyzed 401 is scaled, the coordinate scale corresponding to the initial coordinate system is determined, the coordinate scale is associated with the initial coordinate system, and the spatial coordinate system 402 is generated.
  • the scale change method includes but is not limited to normalization processing and coordinate conversion processing (i.e., the scale of the image to be analyzed 401 is changed by a coordinate conversion function), etc.
  • the image to be analyzed 401 is a 100 ⁇ 50 image
  • a spatial coordinate system 402 is generated based on the size of the image to be analyzed 401.
  • the spatial coordinate system can be used to represent the position of any point in the spatial coordinate system in the image to be analyzed 401, such as (0.1, 0.2) in the spatial coordinate system represents the pixel point (10, 10) in the image to be analyzed 401.
  • the coordinate scale is used to represent the coordinate association relationship between the initial coordinate system and the image to be analyzed, that is, it can represent the position of any point in the initial coordinate system in the image to be analyzed, or the position of any point in the image to be analyzed in the initial coordinate system.
  • any point in the basic cloud map data constructed by the spatial coordinate system can find the corresponding pixel point in the image to be analyzed. That is, it can be considered that when the images to be analyzed are different, the initial coordinate system is the same.
  • the spatial coordinate system is mainly used to map the image to be analyzed to the initial coordinate system, that is, to carry the coordinate scale.
  • the initial coordinate system is composed of a horizontal axis of "0-1" and a vertical axis of "0-1".
  • the image to be analyzed 401 is For an image of 100 ⁇ 50, the coordinate scale includes “width: 0.01, height: 0.02”, that is, the pixel point at position ( x1 , y1 ) in the image to be analyzed 401 has a position ( x1 ⁇ 0.01, y1 ⁇ 0.02) in the initial coordinate system, and the point at position ( x2 , y2 ) in the initial coordinate system has a position ( x2 /0.01, y2 /0.02) in the image to be analyzed.
  • Figure 4b is an example of cloud map construction provided by an embodiment of the present application.
  • an initial coordinate system 403 can be constructed, and an image 404 to be analyzed can be obtained.
  • the coordinate scale between the initial coordinate system 403 and the image 404 to be analyzed is determined, and the coordinate scale is associated with the initial coordinate system 403 to obtain a spatial coordinate system.
  • the image to be analyzed is analyzed, and the analysis result is mapped to the spatial coordinate system to obtain initial unit cloud map data.
  • the image to be analyzed refers to an image carrying cloud information.
  • the computer device can extract cloud information from the image to be analyzed to obtain image cloud information. Based on the distribution of the image cloud information in the image to be analyzed, the image cloud information is mapped to the spatial coordinate system to obtain initial unit cloud map data. For example, the distribution of the image cloud information in the spatial coordinate system can be determined based on the distribution of the image cloud information in the image to be analyzed and the coordinate scale; based on the distribution of the image cloud information in the spatial coordinate system, the image cloud information is mapped to the spatial coordinate system to obtain initial unit cloud map data.
  • the image cloud information can be mapped to the spatial coordinate system 403 based on the distribution of the image cloud information in the spatial coordinate system 403 to obtain initial unit cloud map data 405.
  • the cloud map data is used to represent information such as the distribution and morphology of the volume cloud from a bird's eye view.
  • the initial unit cloud map data can be determined as the basic cloud map data. Since the cloud sampling position information of the sampling point can be mapped to the initial unit cloud map data when the cloud map data is subsequently sampled, the initial unit cloud map data can be directly used as the basic cloud map data and as the cloud map data for subsequent sampling. This can reduce the amount of cloud map data that needs to be maintained, save a lot of space for fine storage of cloud map data, and thus improve the efficiency of cloud map processing.
  • the following method can be used to achieve seamless continuation of the initial unit cloud map data based on the initial unit cloud map data to generate basic cloud map data: based on the initial unit cloud map data, the initial unit cloud map data is seamlessly continued, including seamless self-connection from top to bottom and from left to right, etc., to generate basic cloud map data.
  • the basic cloud map data refers to infinite cloud map data. For example, see Figure 5, which is another schematic diagram of a cloud map construction scene provided by an embodiment of the present application.
  • the computer device can seamlessly continue the initial unit cloud map data 501 based on the initial unit cloud map data 501, that is, continuously copy the initial unit cloud map data 501, and seamlessly connect the copied initial unit cloud map data with the initial unit cloud map data 501 at the beginning (before copying), as shown in the directions indicated by the hollow arrows shown in Figure 5, and continuously seamlessly connect the existing initial unit cloud map data, so as to obtain an infinite basic cloud map data 502.
  • the number of images to be analyzed is N, where N is a positive integer.
  • the initial unit cloud map data can be seamlessly continued on the basis of the initial unit cloud map data to generate basic cloud map data in the following manner: according to the initial unit cloud map data corresponding to the N images to be analyzed, the hierarchical cloud map data corresponding to the N images to be analyzed are determined; based on the height ranges corresponding to the N images to be analyzed, the cloud map height ranges corresponding to the N hierarchical cloud map data are determined; based on the cloud map height ranges corresponding to the N hierarchical cloud map data, the N hierarchical cloud map data are combined to obtain basic cloud map data.
  • the number of images to be analyzed is N, and N is a positive integer.
  • the initial unit cloud map data is seamlessly continued.
  • the N initial unit cloud map data can be seamlessly continued on the basis of the initial unit cloud map data corresponding to the N images to be analyzed, respectively, to generate N hierarchical cloud map data.
  • the generation method of each hierarchical cloud map data can refer to the generation method of the basic cloud map data 502. Taking the generation of the first hierarchical cloud map data as an example, in the initial unit cloud map data corresponding to the first image to be analyzed, On the basis of the data (i.e.
  • the first initial unit cloud map data is seamlessly continued, that is, the first initial unit cloud map data is continuously copied, and the copied initial unit cloud map data is seamlessly connected with the first initial unit cloud map data at the beginning (before copying), and the existing first initial unit cloud map data is continuously seamlessly connected, so that an infinitely large basic cloud map data, i.e. the first level cloud map data, can be obtained; through the above method, the second, third, ..., Nth level cloud map data can be obtained, that is, N level cloud map data need to be determined.
  • the cloud map height ranges corresponding to the N levels of cloud map data are determined, wherein the height range corresponding to each image to be analyzed can be provided by the user who provides the image to be analyzed, or directly set manually; or, the N images to be analyzed can be input into the height analysis model, in which the cloud information features corresponding to the N images to be analyzed are analyzed, the cloud information features are highly matched, and the height ranges corresponding to the N images to be analyzed are determined.
  • the N levels of cloud map data are combined to obtain basic cloud map data.
  • the cloud map height ranges corresponding to the N levels of cloud map data are "0-500 meters”, “500 meters-1500 meters” and “above 1500 meters”, which means that the cloud map height ranges corresponding to the N levels of cloud map data are "0-500 meters”, “500 meters-1500 meters” and "above 1500 meters”, etc.
  • the basic cloud map data may be a two-dimensional cloud map data, or may include N levels of cloud map data carrying height information, etc.
  • Step S302 taking the first viewpoint position as the origin, constructing a local coordinate system corresponding to the first viewpoint position, obtaining sampling point position information of the first sampling point in the local coordinate system corresponding to the first viewpoint position, performing position conversion on the sampling point position information, and obtaining cloud sampling position information.
  • the local coordinate system corresponding to the first viewpoint position refers to a coordinate system consisting of three coordinate axes constructed by the first viewpoint position and taking the first viewpoint position as the origin.
  • step S302 of constructing a local coordinate system corresponding to the first viewpoint position with the first viewpoint position as the origin can be achieved as follows: in the i-th viewpoint movement frame, when i is an initial value, the first viewpoint position i is determined based on the first geographic coordinate point, the first coordinate axis i is determined based on the second geographic coordinate point and the first geographic coordinate point, the second coordinate axis i is constructed in the direction from the center point of the object model to the first viewpoint position i, the third coordinate axis i is determined based on the first coordinate axis i and the second coordinate axis i, and the first coordinate axis i, the second coordinate axis i and the third coordinate axis i are combined to form a local coordinate system i corresponding to the first viewpoint position i.
  • the first geographic coordinate point can be a coordinate obtained arbitrarily from the surface of the object model of the object to be analyzed, or it can be the coordinate of the initial point on the surface of the object model. If the object model is the earth, the first geographic coordinate point can be the point of world coordinates (0, 0, 0); if the object model is a model of a virtual object in an application, the first geographic coordinate point can be the coordinate of the initial point of the application, etc.
  • the first viewpoint position i can be determined as the origin, and the first coordinate axis i can be determined based on the second geographic coordinate point and the first geographic coordinate point.
  • determining the first coordinate axis i based on the second geographic coordinate point and the first geographic coordinate point can be achieved in the following manner: obtaining the tangent plane of the object model at the first geographic coordinate point, obtaining the projection length of the second geographic coordinate point in the tangent plane, and when the projection length of the second geographic coordinate point in the tangent plane is not 0, determining the direction from the first geographic coordinate point to the second geographic coordinate point as the first coordinate axis i; when the projection length of the second geographic coordinate point in the tangent plane is 0, moving the second geographic coordinate point along the tangent plane to obtain a third geographic coordinate, and when the projection length of the third geographic coordinate on the tangent plane is not 0, determining the direction from the first geographic coordinate point to the third geographic coordinate as the first coordinate axis i.
  • the projection length is used to represent the distance between the projection point of the second geographic coordinate point in the tangent plane and the first geographic coordinate point, wherein the tangent plane refers to a plane that is tangent to the surface of the object model and the tangent point is the first geographic coordinate point.
  • the direction from the first geographic coordinate point to the second geographic coordinate point is determined as the first coordinate axis i; if the projection length is 0, it means that the second geographic coordinate point is located on the perpendicular line of the tangent plane and cannot be used as the first coordinate axis i, then the third geographic coordinate is obtained, and when the projection length of the third geographic coordinate on the tangent plane is not 0, the direction from the first geographic coordinate point to the third geographic coordinate is determined as the first coordinate axis i.
  • the third geographic coordinate point is (0, 1, 0), and the direction from the first geographic coordinate point to the third geographic coordinate point is determined as the first coordinate axis i. Since the second geographic coordinate point is located on the vertical line of the tangent plane, specifically on the coordinate axis perpendicular to the tangent plane, the third geographic coordinate point obtained after moving the second geographic coordinate point along the tangent plane will not be located on the coordinate axis perpendicular to the tangent plane, and the first coordinate axis i can be determined based on this.
  • the viewpoint movement frame is used to indicate that when a local coordinate system is constructed for the surface of the object model to be analyzed, the stage of a local coordinate system construction can be considered as a viewpoint movement frame.
  • Different viewpoint moving frames correspond to different first viewpoint positions.
  • the cloud map analysis of the object to be analyzed can be approximated as the analysis process of the surface of the object model of the object to be analyzed rolling in a plane.
  • a local coordinate system is constructed with a first viewpoint position as the initial point, and then based on the initial point, the surface of the object model of the object to be analyzed is continuously offset to obtain the next first viewpoint position, and the local coordinate system is determined until the surface traversal of the object model of the object to be analyzed is completed.
  • the construction process of each local coordinate system can be considered as a viewpoint moving frame.
  • the second coordinate axis i is constructed from the direction from the center point of the object model to the first viewpoint position i, that is, the vector o in the direction from the center point of the object model to the first viewpoint position i is determined as the second coordinate axis i; the third coordinate axis i is determined based on the first coordinate axis i and the second coordinate axis i, specifically, the cross product of the vectors corresponding to the first coordinate axis i and the second coordinate axis i is determined as the third coordinate axis i.
  • the first coordinate axis i, the second coordinate axis i and the third coordinate axis i form the local coordinate system i corresponding to the first viewpoint position i;
  • the center point of the object model refers to the center point of the object model for cloud map analysis.
  • the local coordinate system i corresponding to the first viewpoint position i can be determined by the first viewpoint position i and the tangent plane of the first viewpoint position i.
  • the first coordinate axis i and the third coordinate axis i are located on the tangent plane corresponding to the first viewpoint position i.
  • the cloud map coordinates of the first viewpoint position i can be determined as the origin coordinates in the local coordinate system i corresponding to the first viewpoint position i.
  • the cloud map coordinates are used to represent the coordinates of the corresponding point (such as the viewpoint position or the acquisition point) in the local coordinate system.
  • the cloud map coordinates of the first viewpoint position i refer to the coordinates of the first viewpoint position i in the local coordinate system i.
  • the default origin coordinates such as (0, 0)
  • the coordinate mapping method refers to a method of mapping geographic coordinates to a local coordinate system, which can be determined manually or by other methods of converting three-dimensional coordinates into two-dimensional coordinates, which is not limited here.
  • the geographic coordinates refer to coordinates in a global coordinate system.
  • the global coordinate system can be the world coordinates on the earth.
  • the global coordinate system can be the coordinate system of a game map in the game application, etc.
  • FIG6 is a schematic diagram of a local coordinate construction scene provided by an embodiment of the present application.
  • the first viewpoint position i in the object model 601 is point P
  • the first viewpoint position i is taken as the origin
  • the second coordinate axis i that is, the direction of the vector o
  • the first coordinate axis i that is, the direction of the vector r
  • the third coordinate axis i that is, the direction of the vector f, is determined.
  • the coordinate scales can be associated with the first coordinate axis i, the second coordinate axis i, and the third coordinate axis i, respectively, to obtain the local coordinate system i corresponding to the first viewpoint position i, wherein the coordinates of the origin i of the local coordinate system i can be recorded as (0, 0).
  • the first coordinate axis i and the third coordinate axis i are located on the tangent plane 602 corresponding to the first viewpoint position i.
  • step S302 of constructing a local coordinate system corresponding to the first viewpoint position with the first viewpoint position as the origin can be achieved as follows: in the i-th viewpoint moving frame, when i is not the initial value, the viewing angle acquisition point of the i-th viewpoint moving frame is determined as the first viewpoint position i, and the first viewpoint position i is taken as the origin, and a second coordinate axis i is constructed in the direction from the center point of the object model to the first viewpoint position i, and a first coordinate axis i is constructed based on the third coordinate axis (i-1) corresponding to the (i-1)-th viewpoint moving frame and the second coordinate axis i, and a third coordinate axis i is constructed based on the first coordinate axis i and the second coordinate axis i, and the first coordinate axis i, the second coordinate axis i and the third coordinate axis i are combined to form a local coordinate system i corresponding to the first viewpoint position i.
  • the third coordinate axis (i-1) can be translated to the first viewpoint position i to obtain the reference third coordinate axis.
  • the first coordinate axis i can be constructed.
  • the cross product of the direction vector corresponding to the reference third coordinate axis and the direction vector corresponding to the second coordinate axis i can be determined as the direction vector of the first coordinate axis i.
  • the first coordinate axis i can be obtained.
  • other methods can also be used to determine the first coordinate axis i, that is, when two coordinate axes are known, the third coordinate axis can be constructed.
  • the third coordinate axis i is constructed based on the first coordinate axis i and the second coordinate axis i, and the first coordinate axis i, the second coordinate axis i and the third coordinate axis i form a local coordinate system i corresponding to the first viewpoint position i, when the viewpoint moves at a near-ground position, it can be regarded as the object model rolling on an infinite plane, and the cloud image data within the visible range changes continuously and And the changing trend is the same as the rolling process, and when rendering, it is generally only necessary to render the clouds within a certain range centered on the viewpoint, and there is no need to observe from a distance in outer space. Therefore, there is no need to directly generate a cloud distribution image covering the entire surface of the object model.
  • the local coordinate system can be dynamically generated based on the viewpoint movement, and only attention will be paid to whether the clouds within the viewpoint range are correctly displaced relative to the camera during the viewpoint movement.
  • the direction and shape of the cloud distribution at the same position some time ago are not sensitive. Therefore, the problem of generating a spherical map on the surface of the sphere can be converted into a problem of rolling the sphere on an infinitely large plane and dynamically generating a plane map near the tangent point (ie, the viewpoint position).
  • the local coordinate system of the current viewpoint movement frame can be constructed based on the previous viewpoint movement frame.
  • the above-mentioned label (i-1) is used to indicate that the corresponding data is the data generated in the (i-1)th viewpoint moving frame, such as the third coordinate axis (i-1) is used to indicate the third coordinate axis in the local coordinate system corresponding to the (i-1)th viewpoint moving frame, and the first viewpoint position (i-1) is used to indicate the first viewpoint position corresponding to the (i-1)th viewpoint moving frame, etc.
  • the viewing angle acquisition point (i.e., viewpoint) at which the i-th viewpoint moving frame is located can be determined as the first viewpoint position i.
  • the first viewpoint position i is point P'.
  • the first viewpoint position i can be considered to be obtained by moving the viewpoint position from the first viewpoint position (i-1).
  • the cloud map coordinates of point P can be recorded as P(U, V)
  • the cloud map coordinates of point P' can be recorded as P'(U', V')
  • the cloud map coordinates of any first viewpoint position used to construct a local coordinate system can be obtained.
  • the cloud map coordinates of any point refer to the coordinates of the point in the corresponding local coordinate system.
  • the length of m is less than or equal to the adjacent frame movement threshold, that is, the length of PP' is less than or equal to the adjacent frame movement threshold.
  • the local coordinate system can be updated.
  • Figure 7 is a schematic diagram of a coordinate system conversion scene provided by an embodiment of the present application.
  • the second coordinate axis i that is, the direction of the vector o'
  • the second coordinate axis i can be constructed from the direction from the object model center point 7011 of the object model 701 to the first viewpoint position i; first assume that the third coordinate axis (i-1) is unchanged, that is, the direction of the vector f is unchanged, and construct the first coordinate axis i, that is, the direction of the vector r', based on the third coordinate axis (i-1) and the second coordinate axis i. Based on the first coordinate axis i and the third coordinate axis i, the third coordinate axis i, that is, the direction of the vector f', is constructed.
  • the first coordinate axis i, the second coordinate axis i and the third coordinate axis i are used to form a local coordinate system i corresponding to the first viewpoint position i.
  • the first coordinate axis i and the third coordinate axis i are located on the tangent plane 702 corresponding to the first viewpoint position i (i.e., point P').
  • the first coordinate axis i, the second coordinate axis i and the third coordinate axis i can be associated with the above coordinate scales respectively to obtain the local coordinate system i corresponding to the first viewpoint position i.
  • the coordinate scale is used to indicate the scale change mode of converting the coordinates on the object model to the local coordinate system.
  • the coordinates of the origin i can be marked as (0, 0).
  • the coordinate values of the local coordinate systems corresponding to different viewpoint positions are the same; or, the object coordinates of the first viewpoint position i can be converted into cloud coordinates, and the cloud coordinates corresponding to the first viewpoint position i are determined as the coordinates of the origin i.
  • the coordinate values of the local coordinate system i on the first coordinate axis i and the third coordinate axis i are determined.
  • the movement distance is very small relative to the object model, and the slight turn generated each time is imperceptible to the human eye. Therefore, at the viewpoint position near the viewpoint acquisition device (such as the camera in the game application, etc.), the subjective feeling of the person is the local coordinate system of the viewpoint position, which changes continuously according to the direction of the viewpoint acquisition device, so that when the local coordinate system is constructed based on the adjacent viewpoint movement frames, the accuracy of cloud map processing can be improved.
  • the viewpoint position near the viewpoint acquisition device such as the camera in the game application, etc.
  • obtaining the sampling point position information of the first sampling point in the local coordinate system corresponding to the first viewpoint position in step S302 can be implemented as follows: obtaining the position information of the first viewpoint position, and obtaining the first sampling point a coordinate offset in the local coordinate system corresponding to the first viewpoint position; and adding the coordinate offset to the position information of the first viewpoint position to determine the sampling point position information of the first sampling point.
  • the first sampling point in the local coordinate system i can be obtained, the sampling coordinates of the first sampling point in the local coordinate system i can be obtained, and the sampling coordinates of the first sampling point in the local coordinate system i can be used as the sampling point position information of the first sampling point.
  • FIG8 is a schematic diagram of a position determination scene provided by an embodiment of the present application.
  • area 801 is used to represent a cross-sectional view at the first viewpoint position P.
  • the position information of the first viewpoint position P i.e., the cloud map coordinates of the first viewpoint position P
  • the coordinate offset of the first sampling point A in the local coordinate system corresponding to the first viewpoint position P i.e., (u A , v A ) is obtained.
  • the coordinate offset is added to the position information of the first viewpoint position to determine the sampling point position information of the first sampling point, which is recorded as A(U+u A , V+v A ). The closer the sampling point is to the surface of the object model, the greater the actual shape distortion will be.
  • the shape distortion of point B is greater than that of point A.
  • the coordinates of the origins of different local coordinate systems are all (0, 0), and the sampling coordinates of the first sampling point in the corresponding local coordinate system can be determined as the sampling point position information of the first sampling point; or, the cloud map coordinates of the first viewpoint position can be determined as the position information of the first viewpoint position, and the sampling coordinates of the first sampling point in the corresponding local coordinate system can be determined as the coordinate offset of the first sampling point in the local coordinate system corresponding to the first viewpoint position, and the coordinate offset can be determined as the sampling point position information of the first sampling point, which is recorded as (u A , v A ), or, the coordinate offset can be added to the position information of the first viewpoint position to determine the sampling point position information of the first acquisition point, which is recorded as (U+u A , V+v A ).
  • the projection distance of the first sampling point A on the r-axis is determined as the coordinate offset u A of the first sampling point A, etc.
  • the coordinates of the origin of each local coordinate system are the cloud map coordinates of the corresponding first viewpoint position, and the sampling coordinates of the first sampling point in the local coordinate system can be obtained, and the sampling coordinates of the first sampling point can be determined as the sampling point position information of the first sampling point.
  • the position conversion of the sampling point position information to obtain the cloud sampling position information in step S302 can be achieved by performing modulo processing on the sampling point position information to obtain the cloud sampling position information, such as performing modulo processing on 1, that is, the decimal place of the sampling point position information can be determined as the cloud sampling position information of the first sampling point.
  • Step S303 based on the cloud sampling position information, the basic cloud image data is sampled to obtain the sampling cloud information of the first sampling point.
  • step S303 may be implemented as follows: obtaining first pixel information of a pixel point of the basic cloud image data at the cloud sampling position information, and determining the first pixel information as the sampling cloud information of the first sampling point.
  • step S303 can also be implemented in the following manner: when the basic cloud map data includes N levels of cloud map data, the computer device can obtain target level cloud map data whose cloud map height range includes the position height in the cloud sampling position information from the N levels of cloud map data, and sample the target level cloud map data based on the cloud sampling position information to obtain sampling cloud information of the first sampling point, such as obtaining second pixel information of the pixel point of the target level cloud map data at the cloud sampling position information, and determining the second pixel information as the sampling cloud information of the first sampling point.
  • step S303 can also be implemented in the following manner: when the latitude of the first viewpoint position is the third latitude threshold, based on the cloud sampling position information, the basic cloud map data is sampled to obtain the first cloud information of the first sampling point; based on the cloud sampling position information, the polar cloud map data is sampled to obtain the second cloud information of the first sampling point; the first cloud information and the second cloud information are fused to obtain the sampled cloud information of the first sampling point.
  • the third latitude threshold can be the junction of the middle and low latitudes and the high latitude.
  • the fourth pixel information of the pixel point of the polar cloud map data at the cloud sampling position information is obtained, and the fourth pixel information is determined as the second cloud information of the first sampling point; the first cloud information and the second cloud information are fused to obtain the sampled cloud information of the first sampling point.
  • the first sampling point after obtaining the sampling cloud information of the first sampling point, can be rendered based on the sampling cloud information of the first sampling point; or, noise data can be added to the sampling cloud information of the first sampling point to obtain the sampling cloud information of the first sampling point.
  • the cloud density information of the sample point is rendered using the cloud density information of the first sampling point.
  • the noise data may be random noise, so that adding noise data on the basis of the basic cloud map data can improve the randomness of the cloud density information of different sampling points without destroying the orderliness of the cloud density information, thereby improving the accuracy of cloud map processing.
  • step S907 of FIG. 9 below.
  • a spatial coordinate system can be constructed, and basic cloud map data can be constructed in the spatial coordinate system; the local coordinate system corresponding to the first viewpoint position is constructed with the first viewpoint position as the origin, the sampling point position information of the first sampling point in the local coordinate system corresponding to the first viewpoint position is obtained, and the sampling point position information is converted to obtain cloud sampling position information; different viewpoint positions correspond to different local coordinate systems; based on the cloud sampling position information, the basic cloud map data is sampled to obtain the sampling cloud information of the first sampling point.
  • a basic cloud map data can be constructed, and then when sampling, the coordinates are determined based on the local coordinate system, that is, it is equivalent to converting the problem of "generating a spherical map on the surface of a sphere” into the problem of "rolling the sphere on a plane and generating a plane map near the tangent point (i.e., sampling of the local coordinate system)", which simplifies the production process and efficiency of cloud map data.
  • based on local sampling when generating cloud map data with higher precision, it is not necessary to consume more overhead and resources, etc., thereby saving resources and improving the accuracy and efficiency of cloud map processing.
  • Figure 9 is a schematic diagram of a cloud map data iterative processing flow provided by an embodiment of the present application. As shown in Figure 9, the process may include the following steps:
  • Step S901 constructing a spatial coordinate system, and constructing basic cloud map data in the spatial coordinate system.
  • step S301 of FIG. 3 In practical applications, reference may be made to the relevant description in step S301 of FIG. 3 , which will not be described in detail here.
  • Step S902 in the i-th video moving frame, taking the first viewpoint position i as the origin, constructing a local coordinate system i corresponding to the first viewpoint position i.
  • the local coordinate system corresponding to the first viewpoint position i refers to a coordinate system with the first viewpoint position i as the origin and composed of three coordinate axes constructed by the first viewpoint position i.
  • Step S903 obtaining a first sampling point, obtaining sampling point position information of the first sampling point in a local coordinate system i corresponding to the first viewpoint position i, performing position conversion on the sampling point position information, and obtaining cloud sampling position information.
  • the computer device may obtain the first sampling point based on the first viewpoint position i, the number of the first sampling points being one or at least two, wherein the first sampling point refers to the viewpoint in the area covered by the viewpoint acquisition device corresponding to the first viewpoint position i.
  • a viewpoint area including the first viewpoint position i may be obtained, and the viewpoint included in the viewpoint area may be determined as the first sampling point, wherein the size of the viewpoint area may be a local sampling size, and the local sampling size may be a default size, or may be determined according to the distance between the first viewpoint positions corresponding to adjacent viewpoint moving frames, etc., and is not limited here.
  • Step S904 based on the cloud sampling position information, sample the basic cloud image data to obtain the sampling cloud information of the first sampling point.
  • the first pixel information at the cloud sampling position information can be obtained from the basic cloud map data, and the first pixel information can be determined as the sampling cloud information of the first sampling point; or, when the basic cloud map data includes N-level cloud map data, the second pixel information at the cloud sampling position information is obtained from the determined first-level cloud map data, and the second pixel information is determined as the sampling cloud information of the first sampling point.
  • the sampling cloud information can represent information such as the concentration of the cloud at the corresponding first sampling point, and can be used to construct cloud map data.
  • Step S905 checking the completion status of the cloud map data.
  • step S907 is executed; if the cloud map processing of the object model is not completed, step S906 is executed.
  • Step S906, i++ simulate the movement of the object model to determine the first viewpoint position i.
  • i++ is used to perform the next viewpoint moving frame.
  • the second viewpoint moving frame becomes the (i-1)-th viewpoint moving frame
  • the (i-1)-th viewpoint moving frame is equivalent to the (i-1)-th viewpoint moving frame.
  • the corresponding first viewpoint position is recorded as the first viewpoint position (i-1).
  • the object model can be simulated to move, determine the tangent point between the moved object model and the simulated plane, and determine the tangent point between the moved object model and the simulated plane as the first viewpoint position i; or, after i++, determine a candidate position range with the first viewpoint position (i--) as the center and the adjacent movement threshold as the radius on the surface of the object model to be analyzed, and randomly select the first viewpoint position i within the candidate position range; or, select the first viewpoint position i within the candidate position range along the viewpoint movement direction, and the viewpoint movement direction is used to indicate the direction of the first viewpoint position i relative to the first viewpoint position (i--).
  • the area covered by the local coordinate system of the first viewpoint position i on the object surface of the object to be analyzed is adjacent to the area covered by the local coordinate system of the first viewpoint position (i--) on the surface of the object model to be analyzed, or the intersecting area is less than or equal to the local repetition threshold.
  • the distance between the first viewpoint position i and the first viewpoint position (i-1) is less than or equal to the adjacent frame movement threshold, and the adjacent frame movement threshold is much smaller than the radius of the object model, that is, the distance between the first viewpoint positions corresponding to any two adjacent viewpoint movement frames is less than or equal to the adjacent frame movement threshold.
  • the local coordinate system corresponding to two adjacent viewpoint movement frames changes less, that is, the direction of the first coordinate axis and the direction of the third coordinate axis will not change suddenly, but gradually change within a range that cannot be perceived by the human eye, so that the observation result of the human eye on the cloud map data sampling is in line with the law of continuous relative motion, thereby improving the accuracy of cloud map processing.
  • Step S907 Render cloud image data based on the sampling cloud information of the first sampling point.
  • the sampling cloud information of all first sampling points can be obtained.
  • M is a positive integer.
  • the sampling cloud information corresponding to the M first sampling points can be combined to obtain the object cloud map data corresponding to the object model.
  • noise data can be added to the sampling cloud information corresponding to the M first sampling points to obtain the cloud density information corresponding to the M first sampling points.
  • the M first sampling points are rendered to obtain the object cloud map data corresponding to the object model.
  • the relevant information of the cloud corresponding to each first sampling point i.e., the sampling cloud information or cloud density information
  • the relevant information of the cloud corresponding to each first sampling point can be constructed into the object cloud map data of the object model, thereby realizing the drawing of the cloud map data of the object to be analyzed.
  • the object cloud map data can be integrated into the navigation simulation system.
  • the object cloud map data can be rendered to simulate the real sky scene, so that the real flight scene can be simulated based on the object cloud map data.
  • the object cloud map data can be integrated into a virtual reality (VR) scene, and the object cloud map data can be rendered in the VR scene, so that users participating in the VR scene can obtain a more realistic scene experience, including a navigation VR scene or a VR game scene, etc.
  • VR virtual reality
  • a navigation VR scene users can experience the real navigation process based on the rendered object cloud map data; in a VR game scene, when cloud map data is needed, the VR game scene can be rendered based on the object cloud map data, and users can participate in the VR game scene, such as air combat VR games, etc.
  • the plane cloud map data within a smaller square area that is, the initial unit cloud map data
  • the initial unit cloud map data can be provided to dynamically generate seamless spherical cloud map data sampling on a global scale during runtime, which can save a lot of space for fine storage of cloud map data and cloud map processing resources, and improve the efficiency and accuracy of cloud map processing.
  • Figure 10 is a schematic diagram of a cloud sampling process provided by an embodiment of the present application. As shown in Figure 10, the process may include the following steps:
  • Step S1001 obtaining the latitude of a first viewpoint position, and constructing a local coordinate system corresponding to the first viewpoint position based on the latitude of the first viewpoint position.
  • basic cloud map data and polar cloud map data are obtained. Specifically, as shown in step S301 of FIG3 , basic cloud map data can be generated. At the pole position of the object model, the pole is used as the origin, and the 0° and 90° meridians are used as the horizontal axis and the vertical axis, respectively, to construct a polar coordinate system, and polar cloud map data is constructed for the polar coordinate system.
  • the latitude of the first viewpoint position may be obtained.
  • the latitude of the first viewpoint position is less than or equal to the first latitude threshold, that is, when the first viewpoint position is located in a medium or low latitude area
  • the longitude and latitude coordinates of the first sampling point are determined as the cloud sampling position information of the first sampling point; or, the longitude and latitude coordinates of the first sampling point are subjected to modulus processing to obtain the cloud sampling position information of the first sampling point
  • step S1003 is executed to determine the basic cloud image data as the cloud image data to be sampled. Based on the cloud sampling position information, the basic cloud image data is sampled to obtain the sampling cloud information of the first sampling point.
  • the process of taking the first viewpoint position as the origin and constructing the local coordinate system corresponding to the first viewpoint position is executed, and step S1002 is further executed.
  • a polar coordinate system is constructed at the pole of the object model, the cloud sampling position information of the first sampling point is obtained in the polar coordinate system, and the polar cloud map data is determined as the basic cloud map data, that is, the polar cloud map data is determined as the cloud map data to be sampled, and step S1003 is executed to sample the basic cloud map data based on the cloud sampling position information to obtain the sampling cloud information of the first sampling point;
  • the object model refers to a model for cloud map analysis.
  • the process of constructing a spatial coordinate system and constructing basic cloud map data in the spatial coordinate system is executed, and further, the basic cloud map data is determined as the cloud map data to be sampled, and step S1002 is executed.
  • the first latitude threshold and the second latitude threshold can be the same.
  • the first latitude threshold and the second latitude threshold are the boundaries between the low and medium latitude regions and the high latitude regions, that is, at this time it can be the third latitude threshold.
  • the first latitude threshold and the second latitude threshold can also be different.
  • the latitude of the first viewpoint position is less than or equal to the first latitude threshold, the longitude and latitude coordinates of the first sampling point are determined as the cloud sampling position information of the first sampling point, the basic cloud image data is determined as the cloud image data to be sampled, and step S1003 is executed; if the latitude of the first viewpoint position is greater than the second latitude threshold, a polar coordinate system is constructed at the pole of the object model, the cloud sampling position information of the first sampling point is obtained in the polar coordinate system, the polar circle cloud image data is determined as the cloud image data to be sampled, and step S1003 is executed. If the latitude of the first viewpoint position is greater than the first latitude threshold and less than or equal to the second latitude threshold, the basic cloud image data is determined as the cloud image data to be sampled, and step 1002 is executed.
  • both the basic cloud map data and the polar cloud map data are determined as the cloud map data to be sampled, and step S1002 is executed.
  • Step S1002 Obtain cloud sampling position information of a first sampling point in a local coordinate system corresponding to a first viewpoint position.
  • step S302 in FIG. 3 which is not limited here.
  • Step S1003 obtaining cloud image data to be sampled, sampling the cloud image data to be sampled based on the cloud sampling position information, and obtaining sampling cloud information of the first sampling point.
  • step S303 of FIG. 3 may be specifically referred to, and no limitation is made here.
  • the cloud map data to be sampled includes basic cloud map data and polar cloud map data
  • the basic cloud map data may be sampled based on the cloud sampling position information to obtain first cloud information of the first sampling point
  • the polar cloud map data may be sampled based on the cloud sampling position information to obtain second cloud information of the first sampling point
  • the first cloud information and the second cloud information may be fused to obtain sampled cloud information of the first sampling point.
  • the latitude of the first viewpoint position Through the latitude of the first viewpoint position, targeted sampling processing is performed, so that in special circumstances, special processing of the cloud map data can be achieved, so that the sampling results of the cloud map data can be seamlessly and smoothly transitioned.
  • the latitude of the first viewpoint position is the third latitude threshold
  • the basic cloud map data and the polar cloud map data are both determined as the cloud map data to be sampled, and certain sampling and fusion operations are performed on the cloud map data on both sides, so that the sampling results of the cloud map data can be seamlessly and smoothly transitioned, thereby improving the accuracy of cloud map processing.
  • FIG. 11 is a schematic diagram of a cloud image processing device provided in an embodiment of the present application.
  • the cloud image processing device may be a computer program (including program code, etc.) running in a computer device.
  • the cloud image processing device may be an application software.
  • the device may be used to execute the corresponding steps in the method provided in an embodiment of the present application.
  • the cloud image processing device 1100 may be used in the computer device in the embodiment corresponding to FIG. 3 .
  • the device may include: a cloud image construction module 11, a local construction module 12, a location determination module 13, and a cloud sampling module 14.
  • a cloud map construction module 11 is configured to construct a spatial coordinate system and construct basic cloud map data in the spatial coordinate system;
  • the local construction module 12 is configured to construct a local coordinate system corresponding to the first viewpoint position with the first viewpoint position as the origin; different viewpoint positions correspond to different local coordinate systems;
  • a position determination module 13 is configured to obtain sampling point position information of the first sampling point in a local coordinate system corresponding to the first viewpoint position, and perform position conversion on the sampling point position information to obtain cloud sampling position information;
  • the cloud sampling module 14 is configured to sample the basic cloud image data based on the cloud sampling position information to obtain a first sampling The sampling cloud information of the point.
  • the cloud map construction module 11 is also configured to obtain candidate cloud map data, and associate the candidate cloud map data with the spatial coordinate system to generate initial unit cloud map data; determine the initial unit cloud map data as basic cloud map data, or seamlessly continue the initial unit cloud map data to generate basic cloud map data.
  • the cloud map construction module 11 includes:
  • An image conversion unit 111 is configured to obtain an image to be analyzed, perform coordinate conversion on an image size of the image to be analyzed, and obtain a spatial coordinate system;
  • the analysis and mapping unit 112 is configured to perform image analysis on the image to be analyzed, map the analysis result to the space coordinate system, and obtain initial unit cloud image data;
  • the cloud map continuation unit 113 is configured to perform seamless continuation of the initial unit cloud map data based on the initial unit cloud map data to generate basic cloud map data; the basic cloud map data refers to infinite cloud map data.
  • the number of images to be analyzed is N, where N is a positive integer;
  • the cloud image continuation unit 113 includes:
  • the level generation subunit 1131 is configured to seamlessly continue the N initial unit cloud image data respectively based on the initial unit cloud image data respectively corresponding to the N images to be analyzed, so as to generate N level cloud image data;
  • the height determination subunit 1132 is configured to determine the cloud image height ranges corresponding to the N level cloud image data respectively based on the height ranges corresponding to the N images to be analyzed respectively;
  • the level combination subunit 1133 is configured to combine the N level cloud map data based on the cloud map height ranges respectively corresponding to the N level cloud map data to obtain basic cloud map data.
  • the local building module 12 includes:
  • the first construction unit 121 is configured to, in the i-th viewpoint moving frame, when i is an initial value, determine the first viewpoint position i based on the first geographic coordinate point, determine the first coordinate axis i based on the second geographic coordinate point and the first geographic coordinate point, construct the second coordinate axis i in the direction from the center point of the object model to the first viewpoint position i, determine the third coordinate axis i based on the first coordinate axis i and the second coordinate axis i, and form the first coordinate axis i, the second coordinate axis i and the third coordinate axis i into a local coordinate system i corresponding to the first viewpoint position i; the center point of the object model refers to the center point of the object model for cloud image parsing;
  • the second construction unit 122 is configured to, when i is not an initial value, determine the viewing angle acquisition point where the i-th viewpoint moving frame is located as the first viewpoint position i, construct the second coordinate axis i in the direction from the center point of the object model to the first viewpoint position i, construct the first coordinate axis i based on the third coordinate axis (i-1) corresponding to the (i-1)-th viewpoint moving frame and the second coordinate axis i, construct the third coordinate axis i based on the first coordinate axis i and the second coordinate axis i, and combine the first coordinate axis i, the second coordinate axis i and the third coordinate axis i to form a local coordinate system i corresponding to the first viewpoint position i.
  • the first construction unit 121 when determining the first coordinate axis i based on the second geographic coordinate point and the first geographic coordinate point, includes:
  • the coordinate projection subunit 1211 is configured to obtain a tangent plane of the object model at a first geographic coordinate point, and obtain a projection length of a second geographic coordinate point in the tangent plane;
  • the coordinate axis determination subunit 1212 is configured to, when the projection length of the second geographic coordinate point in the tangent plane is not 0, determine the direction from the first geographic coordinate point to the second geographic coordinate point as the first coordinate axis i; when the projection length of the second geographic coordinate point in the tangent plane is 0, move the second geographic coordinate point along the tangent plane to obtain a third geographic coordinate, and when the projection length of the third geographic coordinate on the tangent plane is not 0, determine the direction from the first geographic coordinate point to the third geographic coordinate as the first coordinate axis i.
  • the location determination module 13 includes:
  • a position acquisition unit 131 is configured to acquire position information of a first viewpoint position
  • An offset obtaining unit 132 is configured to obtain a coordinate offset of the first sampling point in a local coordinate system corresponding to the first viewpoint position;
  • a position determination unit 133 configured to add a coordinate offset to the position information of the first viewpoint position to determine the sampling point position information of the first sampling point;
  • the position conversion unit 134 is configured to perform modulo processing on the sampling point position information to obtain cloud sampling position information.
  • the basic cloud image data includes N levels of cloud image data; the cloud sampling module 14 includes:
  • the level determination unit 141 is configured to obtain, from the N levels of cloud image data, target level cloud image data whose cloud image height range includes the location height in the cloud sampling location information;
  • the hierarchical sampling unit 142 is configured to sample the target hierarchical cloud image data based on the cloud sampling position information to obtain the sampling cloud information of the first sampling point.
  • the apparatus 1100 further includes:
  • the sampling calling module 15 is configured to determine the longitude and latitude coordinates of the first sampling point as the cloud sampling position information of the first sampling point when the latitude of the first viewpoint position is less than or equal to the first latitude threshold, and execute the step of sampling the basic cloud map data based on the cloud sampling position information to obtain the sampling cloud information of the first sampling point.
  • the local calling module 16 is configured to execute a process of constructing a local coordinate system corresponding to the first viewpoint position with the first viewpoint position as an origin when the latitude of the first viewpoint position is greater than a first latitude threshold.
  • the apparatus 1100 further includes:
  • the pole processing module 17 is configured to construct a pole coordinate system at the pole of the object model if the latitude of the first viewpoint position is greater than the second latitude threshold, obtain the cloud sampling position information of the first sampling point in the pole coordinate system, determine the polar circle cloud map data as the basic cloud map data, and perform a process of sampling the basic cloud map data based on the cloud sampling position information to obtain the sampling cloud information of the first sampling point;
  • the object model refers to a model for performing cloud map analysis;
  • the cloud map calling module 18 is configured to execute the process of constructing a spatial coordinate system and constructing basic cloud map data in the spatial coordinate system if the first viewpoint position is less than the second latitude threshold.
  • the cloud sampling module 14 includes:
  • the first sampling unit 143 is configured to sample the basic cloud image data based on the cloud sampling position information to obtain the first cloud information of the first sampling point if the latitude of the first viewpoint position is the third latitude threshold;
  • the second sampling unit 144 is configured to sample the polar cloud image data based on the cloud sampling position information to obtain the second cloud information of the first sampling point;
  • the sampling fusion unit 145 is configured to fuse the first cloud information and the second cloud information to obtain the sampling cloud information of the first sampling point.
  • the apparatus 1100 further includes:
  • the information processing module 19 is configured to add noise data to the sampled cloud information of the first sampling point to obtain the cloud density information of the first sampling point;
  • the sampling and rendering module 20 is configured to render the first sampling point using the cloud density information of the first sampling point.
  • the embodiment of the present application provides a cloud map processing device, which can construct a spatial coordinate system, construct basic cloud map data in the spatial coordinate system; take the first viewpoint position as the origin, construct the local coordinate system corresponding to the first viewpoint position, obtain the sampling point position information of the first sampling point in the local coordinate system corresponding to the first viewpoint position, perform position conversion on the sampling point position information, and obtain cloud sampling position information; different viewpoint positions correspond to different local coordinate systems; based on the cloud sampling position information, sample the basic cloud map data to obtain the sampling cloud information of the first sampling point.
  • a basic cloud map data can be constructed, and then when sampling, the coordinates are determined based on the local coordinate system, that is, it is equivalent to converting the problem of "generating a spherical map on the surface of a sphere” into the problem of "rolling the sphere on a plane and generating a plane map near the tangent point (i.e., sampling of the local coordinate system)", which simplifies the production process and efficiency of cloud map data.
  • based on local sampling when generating cloud map data with higher precision, it is not necessary to consume more overhead and resources, etc., thereby saving resources and improving the accuracy and efficiency of cloud map processing.
  • the computer device in the embodiment of the present application may include: one or more processors 1201, a memory 1202, and an input-output interface 1203.
  • the processor 1201, the memory 1202, and the input-output interface 1203 are connected via a bus 1204.
  • the memory 1202 is used to store a computer program, which includes program instructions, and the input-output interface 1203 is used to receive data and output data, such as for data interaction between a computer device and a business device; the processor 1201 is used to execute the program instructions stored in the memory 1202.
  • the processor 1201 may perform the following operations:
  • Construct a spatial coordinate system and construct basic cloud map data in the spatial coordinate system take the first viewpoint position as the origin and construct The local coordinate system corresponding to the first viewpoint position is obtained, the sampling point position information of the first sampling point in the local coordinate system corresponding to the first viewpoint position is obtained, and the sampling point position information is converted to obtain cloud sampling position information; different viewpoint positions correspond to different local coordinate systems; based on the cloud sampling position information, the basic cloud map data is sampled to obtain the sampling cloud information of the first sampling point.
  • the processor 1201 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • CPU central processing unit
  • DSP digital signal processors
  • ASIC application-specific integrated circuits
  • FPGA field-programmable gate arrays
  • a general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
  • the memory 1202 may include a read-only memory and a random access memory, and provide instructions and data to the processor 1201 and the input/output interface 1203. A portion of the memory 1202 may also include a non-volatile random access memory. For example, the memory 1202 may also store information about the device type.
  • the computer device can execute the implementation methods provided by the various steps in FIG. 3 through its built-in functional modules.
  • the implementation methods provided by the various steps in FIG. 3 please refer to the implementation methods provided by the various steps in FIG. 3 , which will not be repeated here.
  • the embodiment of the present application provides a computer device, including: a processor, an input and output interface, and a memory.
  • the processor obtains the computer program in the memory, executes the various steps of the method shown in Figure 3, and performs cloud image processing operations.
  • the embodiment of the present application realizes that a spatial coordinate system can be constructed, and basic cloud image data can be constructed in the spatial coordinate system; a local coordinate system corresponding to the first viewpoint position is constructed with the first viewpoint position as the origin, and the sampling point position information of the first sampling point in the local coordinate system corresponding to the first viewpoint position is obtained, and the sampling point position information is converted to obtain cloud sampling position information; different viewpoint positions correspond to different local coordinate systems; based on the cloud sampling position information, the basic cloud image data is sampled to obtain the sampling cloud information of the first sampling point.
  • a basic cloud map data can be constructed, and then when sampling, the coordinates are determined based on the local coordinate system.
  • it is equivalent to converting the problem of "generating a spherical map on the surface of a sphere” into the problem of "rolling the sphere on a plane and generating a plane map near the tangent point (i.e., sampling of the local coordinate system)".
  • based on local sampling when generating cloud map data with higher precision, there is no need to consume more expenses and resources, thereby saving resources and improving the accuracy and efficiency of cloud map processing.
  • the embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and the computer program is suitable for being loaded by the processor and executing the cloud image processing method provided by each step in Figure 3.
  • a computer-readable storage medium which stores a computer program
  • the computer program is suitable for being loaded by the processor and executing the cloud image processing method provided by each step in Figure 3.
  • the implementation method provided by each step in Figure 3 which will not be repeated here.
  • the description of the beneficial effects of using the same method will not be repeated.
  • the computer program can be deployed to be executed on one computer device, or on multiple computer devices located at one location, or on multiple computer devices distributed in multiple locations and interconnected by a communication network.
  • the computer-readable storage medium may be the cloud image processing device provided in any of the aforementioned embodiments or the internal storage unit of the computer device, such as the hard disk or memory of the computer device.
  • the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (smart media card, SMC), a secure digital (secure digital, SD) card, a flash card (flash card), etc. equipped on the computer device.
  • the computer-readable storage medium may also include both the internal storage unit of the computer device and an external storage device.
  • the computer-readable storage medium is used to store the computer program and other programs and data required by the computer device.
  • the computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
  • the embodiment of the present application also provides a computer program product or computer program, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium.
  • the processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the various optional methods in Figure 3, and realizes the construction of a basic cloud map data.
  • the coordinates are determined based on the local coordinate system.
  • it is equivalent to converting the problem of "generating a spherical map on the surface of a sphere” into "rolling the sphere on a plane and generating a plane map near the tangent point (i.e., sampling of the local coordinate system)".
  • the problem of cloud map data production is simplified and the efficiency is improved.
  • based on local sampling it does not need to consume more expenses and resources when generating cloud map data with higher precision, thereby saving resources and improving the accuracy and efficiency of cloud map processing.
  • each process and/or box of the method flow chart and/or structural schematic diagram, as well as the combination of the process and/or box in the flow chart and/or block diagram can be implemented by computer program instructions.
  • These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable cloud image processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable cloud image processing device produce a device for implementing the function specified in one process or multiple processes of the flow chart and/or one box or multiple boxes of the structural diagram.
  • These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable cloud image processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the function specified in one process or multiple processes of the flow chart and/or one box or multiple boxes of the structural diagram.
  • These computer program instructions may also be loaded onto a computer or other programmable cloud image processing device so that a series of operating steps are executed on the computer or other programmable device to produce computer-implemented processing, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the structure diagram.
  • the modules in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

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Abstract

本申请实施例公开了一种云图处理方法、装置、计算机及可读存储介质，该方法包括：构建空间坐标系，在空间坐标系中构建基础云图数据；以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系，获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息；不同的视点位置所对应的局部坐标系不同；基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。

Description

云图处理方法、装置、计算机设备、计算机可读存储介质及计算机程序产品
相关申请的交叉引用
本申请实施例基于申请号为202310605898.4、申请日为2023年05月26日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请实施例作为参考。
技术领域
本申请涉及计算机技术领域，尤其涉及一种云图处理方法、装置、计算机设备、计算机可读存储介质及计算机程序产品。
背景技术
目前多数云图主要是针对平面地表，通常生成平面贴图与在球体表面应用平面贴图，生成球体表面贴图。在这一过程中，一般是通过人工或程序生成一个无限大的平面图，当需要采样地球表面某一点的云图时，将该位置的经纬度投影变化到该平面图上所对应的平面坐标，用该平面坐标在无限大的平面云图上进行采样，这一方式难以保障平面坐标均匀分布，而且会在高纬度地区出现畸变，导致经纬度投影的结果准确性较低。或者，直接通过建模工具生成一整个球体表面贴图，这一方式会导致精度与数据量的矛盾难以平衡，生成精度高的平面图需要耗费较大的存储开销及生成资源，导致云图处理效率较低；生成精度较低的平面图，则使得平面图中的细节缺失，导致云图处理准确性较低。
发明内容
本申请实施例提供了一种云图处理方法、装置、计算机设备、计算机可读存储介质及计算机程序产品，能够提高云图处理的准确性及效率。
本申请实施例提供了一种云图处理方法，该方法包括：
构建空间坐标系，在空间坐标系中构建基础云图数据；
以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系，获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息；不同的视点位置所对应的局部坐标系不同；
基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。
本申请实施例提供了一种云图处理装置，该装置包括：
云图构建模块，配置为构建空间坐标系，在空间坐标系中构建基础云图数据；
局部构建模块，配置为以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系；不同的视点位置所对应的局部坐标系不同；
位置确定模块，配置为获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息；
云采样模块，配置为基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。
本申请实施例提供了一种计算机设备，包括处理器、存储器、输入输出接口；
处理器分别与存储器和输入输出接口相连，其中，输入输出接口用于接收数据及输出数据，存储器用于存储计算机程序，处理器用于调用该计算机程序，以使包含该处理器的计算机设备执行本申请实施例提供的云图处理方法。
本申请实施例提供了一种计算机可读存储介质，计算机可读存储介质存储有计算机程序， 该计算机程序适于由处理器加载并执行，以使得具有该处理器的计算机设备执行本申请实施例提供的云图处理方法。
本申请实施例提供了一种计算机程序产品或计算机程序，该计算机程序产品或计算机程序包括计算机指令，该计算机指令存储在计算机可读存储介质中。计算机设备的处理器从计算机可读存储介质读取该计算机指令，处理器执行该计算机指令，使得该计算机设备执行本申请实施例提供的云图处理方法。
应用本申请实施例，将具有如下有益效果：
在本申请实施例中，可以构建空间坐标系，在空间坐标系中构建基础云图数据；以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系，获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息；不同的视点位置所对应的局部坐标系不同；基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。通过以上过程，可以构建一个基础云图数据，然后在进行采样时，是基于局部坐标系进行的坐标确定，也就是说，相当于把“在球体表面生成球形贴图”的问题，转化为“球体在平面上进行滚动，并生成切点附近的平面贴图(即局部坐标系的采样)”的问题，简化了云图数据的制作流程与效率，而且，基于局部采样，使得在生成精度较高的云图数据时，也无需耗费较多的开销和资源等，从而节省资源，提高云图处理的准确性及效率。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种云图处理的网络交互架构图；
图2是本申请实施例提供的一种云图处理场景示意图；
图3是本申请实施例提供的一种云图处理的方法流程图；
图4a是本申请实施例提供的一种云图构建场景示意图；
图4b是本申请实施例提供的一种云图构建示例；
图5是本申请实施例提供的另一种云图构建场景示意图；
图6是本申请实施例提供的一种局部坐标构建场景示意图；
图7是本申请实施例提供的一种坐标系转换场景示意图；
图8是本申请实施例提供的一种位置确定场景示意图；
图9是本申请实施例提供的一种云图数据迭代处理流程示意图；
图10是本申请实施例提供的一种云采样流程示意图；
图11是本申请实施例提供的一种云图处理装置示意图；
图12是本申请实施例提供的一种计算机设备的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
其中，若在本申请中需要收集对象(如用户等)数据，则在收集前、收集中，显示提示界面或者弹窗，该提示界面或者弹窗用于提示用户当前正在搜集XXXX数据，仅仅在获取到用户对该提示界面或者弹窗发出确认操作后，开始执行数据获取的相关的步骤，否则结束。 而且，对于获取到的用户数据，会在合理合法的场景或用途等上进行使用。在一些需要使用用户数据但未得到用户授权的场景中，还可以向用户请求授权，在授权通过时，再使用用户数据。其中，用户数据的使用符合法律法规的相关规定。
在本申请实施例中，请参见图1，图1是本申请实施例提供的一种云图处理的网络交互架构图，如图1所示，计算机设备101可以基于位于对象模型的表面的各个视点位置进行局部坐标系构建，并以局部坐标系对各个采样点进行云信息确定，从而实现对对象模型的云图解析。可选的，计算机设备101可以接收任意一个或多个业务设备的云图数据渲染请求，基于该云图数据渲染请求确定对各个采样点进行云信息确定，并将确定的云信息发送至云图数据渲染请求所对应的业务设备中。其中，该业务设备的数量可以为一个或多个，如图1中所示的业务设备102a、业务设备102b及业务设备102c等。其中，对象模型是指进行云图解析的待解析对象的模型，该待解析对象是指进行云图解析的对象，可以是实体对象，也可以是应用程序中的虚拟对象等，该实体对象可以是但不限于地球或其他表面较大的对象，该虚拟对象可以是但不限于应用程序中需要进行云图处理的对象，如游戏应用中的虚拟星球等。通过以上过程，可以基于局部坐标系对各个采样点进行云图解析，使得对于“在球体表面生成球形贴图”的问题得以简化为“局部贴图”的问题，简化了云图处理的流程，从而可以提高云图处理的准确性及效率。
具体的，请参见图2，图2是本申请实施例提供的一种云图处理场景示意图。如图2所示，计算机设备可以构建空间坐标系201，该空间坐标系201包括横向坐标轴(u轴)、纵向坐标轴(v轴)以及坐标系原点(ori点)，该空间坐标系201可以认为是一个平面坐标系，例如，可以以两个云分布范围(如0～1等)构建横向坐标轴及纵向坐标轴，基于横向坐标轴与纵向坐标轴构建空间坐标系201。进一步，可以在空间坐标系201中构建基础云图数据202。其中，本申请实施例中的云图数据(Cloud-map/Weather-map/Weather-texture)用于表示俯视视角下，体积云的分布及形态等信息，也就是云图。进一步，可以以第一视点位置203作为原点，构建第一视点位置203所对应的局部坐标系204，该局部坐标系204可以认为是基于对象模型在第一视点位置203处的切平面生成的，可以获取第一采样点205在第一视点位置203所对应的局部坐标系204中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息，该云采样位置信息用于表示第一采样点205在基础云图数据202中的位置。其中，不同的视点位置所对应的局部坐标系不同。可以基于云采样位置信息，对基础云图数据202进行采样，得到第一采样点205的采样云信息。通过局部坐标系构建，使得每个采样点均在临近的视点位置所对应的局部坐标系中进行云采样位置信息的确定，而局部坐标系的构建，使得每个局部坐标系所对应的区域范围较小，每个局部坐标系下的平面坐标分布较为均匀，从而可以将各个采样点更好更准确地映射至基础云图数据中进行采样，提高了云图处理的准确性及效率。
可以理解的是，本申请实施例中所提及的计算机设备包括但不限于终端设备或服务器。换句话说，计算机设备可以是服务器或终端设备，也可以是服务器和终端设备组成的系统。其中，以上所提及的终端设备可以是一种电子设备，包括但不限于手机、平板电脑、台式电脑、笔记本电脑、掌上电脑、车载设备、增强现实/虚拟现实(Augmented Reality/Virtual Reality，AR/VR)设备、头盔显示器、智能电视、可穿戴设备、智能音箱、数码相机、摄像头及其他具备网络接入能力的移动互联网设备(mobile internet device，MID)，或者火车、轮船、飞行等场景下的终端设备等。如图1中所示，终端设备可以是一种笔记本电脑(如业务设备102b所示)、手机(如业务设备102c所示)或车载设备(如业务设备102a所示)等，图1仅例举出部分的设备，可选的，该业务设备102a是指位于交通工具103中的设备，业务设备102a可以用于显示云图数据等。其中，以上所提及的服务器可以是独立的物理服务器，也可以是多个物理服务器构成的服务器集群或者分布式系统，还可以是提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、车路协同、内容分发网络(Content Delivery Network，CDN)、以及大数据和人工智能平台等基础云计算 服务的云服务器。
本申请实施例中所涉及的数据可以存储在计算机设备中，或者可以基于云存储技术或区块链网络对该数据进行存储，在此不做限制。
请参见图3，图3是本申请实施例提供的一种云图处理的方法流程图。如图3所示，该云图处理过程包括如下步骤：
步骤S301，构建空间坐标系，在空间坐标系中构建基础云图数据。
在一些实施例中，计算机设备可以通过如下方式实现步骤S301中的在空间坐标系中构建基础云图数据：获取候选云图数据，并将候选云图数据与空间坐标系进行关联，生成初始单位云图数据；将初始单位云图数据确定为基础云图数据，或者，对初始单位云图数据进行无缝延续生成基础云图数据。
在实际应用中，计算机设备可先构建一个空间坐标系，在空间坐标系中构建初始单位云图数据，并将初始单位云图数据确定为基础云图数据；或者，在初始单位云图数据基础上，对初始单位云图数据进行无缝延续，包括上下无缝自接及左右无缝自接等，生成基础云图数据。例如，可以构建一个空间坐标系，该空间坐标系以第一取值范围构建横坐标，以第二取值范围构建纵坐标，将横坐标与纵坐标组成空间坐标系；获取候选云图数据，将候选云图数据与空间坐标系进行关联，生成初始单位云图数据。其中，对候选云图数据与空间坐标系进行关联，是指确定候选云图数据中的各个像素点在空间坐标系中的位置，候选云图数据是指由人工绘制的精细云图数据，或者通过模型生成并由人工优化的精细云图数据等，该候选云图数据的尺寸较小，如候选云图数据的尺寸小于或等于单位尺寸阈值，如50米×50米等，具体可以根据云图数据绘制成本进行确定，例如，当云图数据制作的成本下降时，可以增加单位尺寸阈值等，使得在生成候选云图数据时只需耗费较少的人工成本和资源，就可以得到所需的云图数据，降低云图数据生成成本。
在一些实施例中，计算机设备可以通过如下方式实现步骤S301：获取待解析图像，对待解析图像的图像尺寸进行坐标转换，得到空间坐标系；对待解析图像进行图像解析，将解析结果映射至空间坐标系，得到初始单位云图数据；在初始单位云图数据基础上，对初始单位云图数据进行无缝延续，生成基础云图数据。
在实际应用中，计算机设备可以获取待解析图像，对待解析图像的图像尺寸进行坐标转换，得到空间坐标系。例如，参见图4a，图4a是本申请实施例提供的一种云图构建场景示意图。如图4a所示，计算机设备可以以待解析图像401的左下角4011作为坐标系原点，基于待解析图像401的宽构建横向坐标轴(u轴)，基于待解析图像401的高构建纵向坐标轴(v轴)，将坐标系原点、横向坐标轴及纵向坐标轴构建初始坐标系，该初始坐标系可以认为是一个二维坐标系，该二维坐标系中的横坐标取值属于第一取值范围，纵坐标取值属于第二取值范围，其中，该第一取值范围与第二取值范围可以认为是默认的纹理坐标范围，即UV坐标范围“0～1”等；对待解析图像401的尺寸进行尺度变化，确定初始坐标系所对应的坐标尺度，将坐标尺度关联至初始坐标系，生成空间坐标系402。该尺度变化方式包括但不限于归一化处理及坐标转换处理(即通过坐标转换函数对待解析图像401的尺寸进行尺度变化)等，例如，待解析图像401为100×50的图像，基于待解析图像401的尺寸生成空间坐标系402，此时，该空间坐标系可以用于表示该空间坐标系中的任意一个点在待解析图像401中的位置，如空间坐标系中的(0.1，0.2)表示待解析图像401中的像素点(10，10)等。其中，该坐标尺度用于表示初始坐标系与待解析图像之间的坐标关联关系，也就是可以表示初始坐标系中的任意一个点在待解析图像中的位置，或者待解析图像中的任意一个点在初始坐标系中的位置，通过在初始坐标系中关联坐标尺度，使得通过空间坐标系所构建的基础云图数据中，任意一个点都可以在待解析图像中找到对应的像素点。也就是可以认为，在待解析图像不同时，初始坐标系是相同的，空间坐标系主要是用于将待解析图像映射至初始坐标系中，也就是携带坐标尺度，如初始坐标系由“0～1”的横坐标轴及“0～1”纵坐标轴组成，待解析图像401为 100×50的图像，则坐标尺度包括“宽：0.01，高：0.02”，也就是，待解析图像401中位置为(x1，y1)的像素点，在初始坐标系中的位置为(x1×0.01，y1×0.02)，初始坐标系中的位置为(x2，y2)的点，在待解析图像中的位置为(x2/0.01，y2/0.02)。
示例性的，参见图4b，图4b是本申请实施例提供的一种云图构建示例。如图4b所示，可以构建初始坐标系403，获取待解析图像404，基于初始坐标系403与待解析图像404，确定初始坐标系403与待解析图像404之间的坐标尺度，将坐标尺度关联至初始坐标系403，得到空间坐标系。
在得到空间坐标系后，对待解析图像进行图像解析，将解析结果映射至空间坐标系，得到初始单位云图数据。其中，该待解析图像是指携带有云信息的图像，计算机设备可以对待解析图像进行云信息提取，得到图像云信息，基于图像云信息在待解析图像中的分布情况，将图像云信息映射至空间坐标系，得到初始单位云图数据。例如，可以基于图像云信息在待解析图像中的分布情况，以及坐标尺度，确定图像云信息在空间坐标系中的分布情况；基于该图像云信息在空间坐标系中的分布情况，将图像云信息映射至空间坐标系，得到初始单位云图数据。如图4b所示，可以基于该图像云信息在空间坐标系403中的分布情况，将图像云信息映射至空间坐标系403中，得到初始单位云图数据405。其中，云图数据用于表示俯视视角下，体积云的分布及形态等信息。
在一些实施例中，在得到初始单位云图数据后，可以将初始单位云图数据确定为基础云图数据，由于后续进行云图数据采样时，对采样点的云采样位置信息，均可以映射至初始单位云图数据中，使得可以直接以初始单位云图数据作为基础云图数据，作为后续采样的云图数据，这样可以减少所需维护的云图数据的数据量，可以节省大量精细存储云图数据的空间，进而提高云图处理的效率。
在一些实施例中，还可通过如下方式实现在初始单位云图数据基础上，对初始单位云图数据进行无缝延续，生成基础云图数据：在初始单位云图数据基础上，对初始单位云图数据进行无缝延续，包括上下无缝自接及左右无缝自接等，生成基础云图数据，此时，该基础云图数据是指无限大的云图数据。例如，可以参见图5，图5是本申请实施例提供的另一种云图构建场景示意图，如图5所示，计算机设备可以在初始单位云图数据501基础上，对初始单位云图数据501进行无缝延续，也就是不断复制初始单位云图数据501，将复制的初始单位云图数据与开始(复制前)的初始单位云图数据501进行无缝连接，如在图5中所示的各个空心箭头所示的方向，不断对已有的初始单位云图数据进行无缝连接，从而可以得到一个无限大的基础云图数据502。
在一些实施例中，待解析图像的数量为N，N为正整数，此时，还可通过如下方式实现在初始单位云图数据基础上，对初始单位云图数据进行无缝延续，生成基础云图数据：根据N个待解析图像分别对应的初始单位云图数据，确定为N个待解析图像分别对应的层级云图数据；基于N个待解析图像分别对应的高度范围，确定N个层级云图数据分别对应的云图高度范围；基于N个层级云图数据分别对应的云图高度范围，对N个层级云图数据进行组合，得到基础云图数据。
在实际应用中，假设待解析图像的数量为N，N为正整数，此时，在初始单位云图数据基础上，对初始单位云图数据进行无缝延续，生成基础云图数据时，可以在N个待解析图像分别对应的初始单位云图数据基础上，对N个初始单位云图数据分别进行无缝延续，生成N个层级云图数据，每个层级云图数据的生成方式可以参见上述基础云图数据502的生成方式，以生成第1个层级云图数据为例，在第1个待解析图像对应的初始单位云图数据(即第1个初始单位云图数据)基础上，对第1个初始单位云图数据进行无缝延续，即不断复制第1个初始单位云图数据，将复制的初始单位云图数据与开始(复制前)的第1个初始单位云图数据进行无缝连接，不断对已有的第1个初始单位云图数据进行无缝连接，从而可以得到一个无限大的基础云图数据，即第1个层级云图数据；通过上述方式，即可得到第2个、第3个、…、第N个层级云图数据，即需确定N个层级云图数据。
在确定N个层级云图数据后，基于N个待解析图像分别对应的高度范围，确定N个层级云图数据分别对应的云图高度范围，其中，每个待解析图像所对应的高度范围可以是由提供待解析图像的用户所提供的，或者直接由人工设定；或者，可以将N个待解析图像分别输入高度解析模型中，在高度解析模型中，解析N个待解析图像分别对应的云信息特征，对云信息特征进行高度匹配，确定N个待解析图像分别对应的高度范围等。基于N个层级云图数据分别对应的云图高度范围，对N个层级云图数据进行组合，得到基础云图数据。例如，假定N为3，N个层级云图数据分别对应的云图高度范围为“0～500米”、“500米～1500米”及“1500米以上”，则表示，N个层级云图数据分别对应的云图高度范围为“0～500米”、“500米～1500米”及“1500米以上”等。
也就是说，该基础云图数据可以是一个二维云图数据，也可以包括携带高度信息的N个层级云图数据等。
步骤S302，以第一视点位置为原点，构建第一视点位置所对应的局部坐标系，获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息。
其中，不同的视点位置所对应的局部坐标系不同，第一视点位置所对应的局部坐标系，是指以第一视点位置为原点，由第一视点位置所构建的三个坐标轴组成的坐标系。
在一些实施例中，步骤S302中的以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系可通过如下方式实现：在第i个视点移动帧中，当i为初始值时，基于第一地理坐标点确定第一视点位置i，基于第二地理坐标点与第一地理坐标点确定第一坐标轴i，以对象模型中心点至第一视点位置i的方向构建第二坐标轴i，基于第一坐标轴i及第二坐标轴i确定第三坐标轴i，将第一坐标轴i、第二坐标轴i及第三坐标轴i，组成第一视点位置i所对应的局部坐标系i。
其中，第一地理坐标点可以是从待解析对象的对象模型的表面任意获取的一个坐标，也可以是对象模型的表面的初始点的坐标，如对象模型为地球，该第一地理坐标点可以是世界坐标(0，0，0)的点，如对象模型为应用程序中的虚拟对象的模型，则该第一地理坐标点可以是应用程序的初始点的坐标等。
进一步，可以将第一视点位置i确定为原点，基于第二地理坐标点与第一地理坐标点确定第一坐标轴i。在一些实施例中，基于第二地理坐标点与第一地理坐标点确定第一坐标轴i可以通过如下方式实现：获取对象模型在第一地理坐标点处的切平面，获取第二地理坐标点在切平面中的投影长度，当第二地理坐标点在切平面中的投影长度不为0时，将第一地理坐标点至第二地理坐标点的方向确定为第一坐标轴i；当第二地理坐标点在切平面中的投影长度为0时，对第二地理坐标点沿所述切平面进行移动，得到第三地理坐标，当所述第三地理坐标在切平面上的投影长度不为0时，将第一地理坐标点至第三地理坐标的方向确定为第一坐标轴i。
其中，该投影长度用于表示第二地理坐标点在切平面中的投影点，与第一地理坐标点之间的距离，其中，切平面是指与对象模型的表面相切，且相切的点为第一地理坐标点的平面。若投影长度不为0，则将第一地理坐标点至第二地理坐标点的方向确定为第一坐标轴i；若投影长度为0，表示第二地理坐标点位于切平面的垂线上，无法作为第一坐标轴i，则获取第三地理坐标，在第三地理坐标在切平面上的投影长度不为0时，将第一地理坐标点至第三地理坐标的方向确定为第一坐标轴i。
例如，第二地理坐标点为(1，0，0)，若第二地理坐标点在切平面上的投影长度为0，则取第三地理坐标(0，1，0)，将第一地理坐标点至第三地理坐标的方向确定为第一坐标轴i，由于第二地理坐标点位于切平面的垂线上，具体是位于垂直于切平面的坐标轴上，因此，对第二地理坐标点沿切平面进行移动后得到的第三地理坐标，不会位于垂直于切平面的坐标轴上，可以基于此进行第一坐标轴i的确定。其中，视点移动帧用于表示对待解析对象的对象模型表面进行局部坐标系构建时，一个局部坐标系构建所处的阶段可以认为是一个视点移 动帧，不同视点移动帧所对应的第一视点位置不同。也就是说，对于待解析对象的云图解析，可以近似为待解析对象的对象模型的表面在一个平面中滚动的过程解析，此时，可以认为是以一个第一视点位置作为初始点进行局部坐标系的构建，再在该初始点基础上，不断在待解析对象的对象模型的表面进行偏移，以得到下一个第一视点位置，进行局部坐标系的确定，直至对待解析对象的对象模型的表面遍历完成，其中，每一个局部坐标系的构建过程可以认为是一个视点移动帧。
以对象模型中心点至第一视点位置i的方向构建第二坐标轴i，也就是，将对象模型中心点到该第一视点位置i的方向上的向量o，确定为第二坐标轴i；基于第一坐标轴i及第二坐标轴i确定第三坐标轴i，具体可以是将第一坐标轴i与第二坐标轴i分别对应的向量的叉积，确定为第三坐标轴i。将第一坐标轴i、第二坐标轴i及第三坐标轴i，组成第一视点位置i所对应的局部坐标系i；对象模型中心点是指进行云图解析的对象模型的中心点。也就是说，以对象模型的任意一个表面位置作为起始点，都有一个平面在该点处与对象模型相切，从而可以以第一视点位置i及第一视点位置i的切平面，确定第一视点位置i所对应的局部坐标系i。其中，该第一坐标轴i与第三坐标轴i，位于第一视点位置i所对应的切平面上。其中，可以将第一视点位置i的云图坐标，确定为第一视点位置i所对应的局部坐标系i中的原点坐标，云图坐标用于表示对应的点(如视点位置或采集点)在局部坐标系中的坐标，例如，第一视点位置i的云图坐标是指第一视点位置i在局部坐标系i中的坐标。在实际应用中，可以将默认原点坐标，如(0，0)，确定为第一视点位置i的云图坐标；或者，可以采用坐标映射方法，将第一视点位置i的地理坐标转换成云图坐标，坐标映射方法是指将地理坐标映射到局部坐标系的方法，可以是人工确定的，也可以是采用其他由三维坐标转换为二维坐标的方法，在此不做限制，其中，地理坐标是指在全局坐标系下的坐标，如在待解析对象为地球时，该全局坐标系可以是地球上的世界坐标，如在待解析对象为游戏应用中的对象时，该全局坐标系可以是游戏应用中的游戏地图的坐标系等。
举例来说，参见图6，图6是本申请实施例提供的一种局部坐标构建场景示意图，如图6所示，假定对象模型601中的第一视点位置i为P点，以第一视点位置i作为原点，以对象模型中心6011至第一视点位置i的方向构建第二坐标轴i，也就是向量o的方向；基于第二地理坐标点与第一地理坐标点确定第一坐标轴i，也就是向量r的方向；进一步，基于第一坐标轴i与第二坐标轴i的叉积，确定第三坐标轴i，也就是向量f的方向。进一步，可以为第一坐标轴i、第二坐标轴i及第三坐标轴i分别关联坐标尺度，得到第一视点位置i所对应的局部坐标系i，其中，该局部坐标系i的原点i的坐标可以记作(0，0)。其中，第一坐标轴i与第三坐标轴i位于第一视点位置i所对应的切平面602上。
在一些实施例中，步骤S302中的以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系可通过如下方式实现：在第i个视点移动帧中，当i不为初始值时，将第i个视点移动帧所处的视角采集点确定为第一视点位置i，以第一视点位置i作为原点，以对象模型中心点至第一视点位置i的方向构建第二坐标轴i，基于第(i-1)个视点移动帧所对应的第三坐标轴(i-1)及第二坐标轴i，构建第一坐标轴i，基于第一坐标轴i及第二坐标轴i，构建第三坐标轴i，将第一坐标轴i、第二坐标轴i及第三坐标轴i，组成第一视点位置i所对应的局部坐标系i。
其中，在构建第一坐标轴i时，可将第三坐标轴(i-1)平移至第一视点位置i处，得到参考第三坐标轴，基于参考第三坐标轴与第二坐标轴i，构建第一坐标轴i，如可以将参考第三坐标轴所对应的方向向量与第二坐标轴i所对应的方向向量的叉积，确定为第一坐标轴i的方向向量，基于第一坐标轴i的方向向量可以得到第一坐标轴i，当然也可以采用其他方式进行第一坐标轴i的确定，即已知两个坐标轴，构建第三个坐标轴的方式。
在基于第一坐标轴i及第二坐标轴i，构建第三坐标轴i，将第一坐标轴i、第二坐标轴i及第三坐标轴i，组成第一视点位置i所对应的局部坐标系i时，当视点在近地位置进行移动时，可视为对象模型在无限大平面上进行滚动，在可视范围内的云图数据是连续变化的，并 且变化趋势与滚动过程相同，而在进行渲染时，一般只需要对以视点为中心的一定范围内的云进行渲染即可，不需要在外太空远距离观察，因此，不需要直接生成覆盖整个对象模型表面的云分布图像，因此可以基于视点移动进行局部坐标系的动态生成，而且，只会关注在视点移动过程中，视点范围内的云是否正确地相对相机进行位移，对同一个位置一段时间之前云分布的方向及形态等并不敏感，故而，可以将球体表面生成球形贴图的问题，转化为球体在无限大的平面上进行滚动，并动态生成切点(即视点位置)附近平面贴图的问题，也就是说，可以在前一个视点移动帧的基础上，构建当前视点移动帧的局部坐标系。其中，上述标号(i-1)用于表示对应的数据是在第(i-1)个视点移动帧中所产生的数据，如第三坐标轴(i-1)用于表示第(i-1)个视点移动帧所对应的局部坐标系中的第三坐标轴，第一视点位置(i-1)用于表示第(i-1)个视点移动帧所对应的第一视点位置等。
在实际应用中，若i不为初始值，则可以将第i个视点移动帧所处的视角采集点(即视点)确定为第一视点位置i，如图6所示，第一视点位置i为P'点，该第一视点位置i可以认为是由第一视点位置(i-1)进行视点位置进行移动得到的，假定第一视点位置(i-1)为P点，移动的向量可以记作m，即m＝PP'。假定P点的云图坐标可以记作P(U，V)，P'点的云图坐标可以记作P'(U'，V')，则可以记作U'＝U+Δu，V'＝V+Δv，可以将第一视点位置i在局部坐标系(i-1)中的坐标(Δu，Δv)，确定为第一视点位置i相对于第一视点位置(i-1)的位置偏移量；在第一视点位置(i-1)的云图坐标上，添加第一视点位置i相对于第一视点位置(i-1)的位置偏移量，得到第一视点位置i的云图坐标，记作P'(U'，V')＝P'(U+Δu，V'＝V+Δv)。同理，可以得到任意一个用于构建局部坐标系的第一视点位置的云图坐标。其中，任意一个点(如第一视点位置或第一采样点等)云图坐标，是指该点在对应的局部坐标系中的坐标。其中，m的长度小于或等于相邻帧移动阈值，也就是PP'的长度小于或等于相邻帧移动阈值，通过该相邻帧移动阈值，限制两个相邻的视点移动帧之间的视点移动距离，即第一视点位置的变化距离，从而使得两个相邻的局部坐标系不会发生突变，提高局部坐标系在云图处理中的容错性，从而提高云图处理的准确性及效率。
在第i个视点移动帧中，P点所对应的向量f的方向与向量r的方向过P点的平面，与对象模型的表面不再是相切关系，为了使得局部坐标系中存在两个坐标轴所构成的过视点位置的平面，始终与对象模型的表面保持相切关系，可以对局部坐标系进行更新。具体的，可以参见图7，图7是本申请实施例提供的一种坐标系转换场景示意图。如图7所示，可以以对象模型701的对象模型中心点7011至第一视点位置i的方向构建第二坐标轴i，即向量o'的方向；先假定第三坐标轴(i-1)不变，也就是向量f的方向不变，基于第三坐标轴(i-1)及第二坐标轴i构建第一坐标轴i，即向量r'的方向。基于第一坐标轴i及第三坐标轴i，构建第三坐标轴i，即向量f'的方向。将第一坐标轴i、第二坐标轴i及第三坐标轴i，组成第一视点位置i所对应的局部坐标系i，此时，可以认为第一坐标轴i与第三坐标轴i位于第一视点位置i(即P'点)所对应的切平面702上。具体的，可以基于第一视点位置i作为原点i，为第一坐标轴i、第二坐标轴i及第三坐标轴i分别关联上述坐标尺度，得到第一视点位置i所对应的局部坐标系i。其中，坐标尺度用于表示将对象模型上的坐标转换到局部坐标系的尺度变化方式。可选的，可以将原点i的坐标记作(0，0)，此时，可以认为不同视点位置所对应的局部坐标系的坐标值是相同的；或者，可以将第一视点位置i的对象坐标转换为云图坐标，将第一视点位置i所对应的云图坐标确定为原点i的坐标，基于坐标尺度，确定局部坐标系i在第一坐标轴i与第三坐标轴i上的坐标取值。其中，在任意相邻的两个视点移动帧中，移动的距离相对于对象模型非常小时，每次产生的微小转向是人眼无法察觉的，因此，在视点采集设备(如游戏应用中的相机等)附近的视点位置下，人的主观感受是该视点位置的局部坐标系，是根据视点采集设备的方向进行相应的连续变化的，使得在基于相邻的视点移动帧进行局部坐标系构建时，可以提高云图处理的准确性。
在一些实施例中，步骤S302中的获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息可通过如下方式实现：获取第一视点位置的位置信息，并获取第一采样点 在第一视点位置所对应的局部坐标系中的坐标偏移量；在第一视点位置的位置信息中添加坐标偏移量，确定第一采样点的采样点位置信息。
在实际应用中，在获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息时，可以获取局部坐标系i下的第一采样点，获取第一采样点在局部坐标系i中的采样坐标，并将第一采样点在局部坐标系i中的采样坐标作为该第一采样点的采样点位置信息。在获取第一视点位置的位置信息，获取第一采样点在第一视点位置所对应的局部坐标系中的坐标偏移量时，举例来说，参见图8，图8是本申请实施例提供的一种位置确定场景示意图，如图8所示，区域801用于表示第一视点位置P处的断面图，假定第一采样点为点A，获取第一视点位置P的位置信息，即第一视点位置P的云图坐标，获取第一采样点A在第一视点位置P所对应的局部坐标系中的坐标偏移量，即(uA，vA)。在第一视点位置的位置信息中添加坐标偏移量，确定第一采样点的采样点位置信息，记作A(U+uA，V+vA)。其中，越靠近对象模型表面的采样点实际的形状畸变会越大，如图8中所示的B点的形状畸变会大于A点的形状畸变，然而在实际的场景中，越靠近对象模型表面的位置，在云图数据中实际所占据的画面比例越小，越容易被更多的前景物遮挡，因此，不会对画面结果的真实感产生影响，可以保障云图处理的准确性。
在一些实施例中，不同局部坐标系的原点的坐标均为(0，0)，可以将第一采样点在对应的局部坐标系中的采样坐标，确定为第一采样点的采样点位置信息；或者，可以将第一视点位置的云图坐标确定为第一视点位置的位置信息，将第一采样点的在对应的局部坐标系中的采样坐标，确定为第一采样点在第一视点位置所对应的局部坐标系中的坐标偏移量，将坐标偏移量确定为第一采样点的采样点位置信息，记作(uA，vA)，或者，可以将第一视点位置的位置信息中添加坐标偏移量，确定第一采集点的采样点位置信息，记作(U+uA，V+vA)。如图8中，将第一采样点A在r轴上的投影距离，确定为第一采样点A的坐标偏移量uA等。在另一些实施例中，每个局部坐标系的原点的坐标是对应的第一视点位置的云图坐标，可以获取第一采样点在局部坐标系中的采样坐标，将该第一采样点的采样坐标确定为第一采样点的采样点位置信息。
在一些实施例中，步骤S302中的对采样点位置信息进行位置转换，得到云采样位置信息，可通过如下方式实现：对采样点位置信息进行取余处理，得到云采样位置信息，如对1进行取余处理，也就是可以将采样点位置信息的小数位，确定为第一采样点的云采样位置信息。
步骤S303，基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。
在一些实施例中，步骤S303可通过如下方式实现：获取基础云图数据在云采样位置信息处的像素点的第一像素信息，并将第一像素信息确定为第一采样点的采样云信息。
在一些实施例中，步骤S303还可以通过如下方式实现：当基础云图数据包括N个层级云图数据时，计算机设备可以从N个层级云图数据中，获取云图高度范围包括云采样位置信息中的位置高度的目标层级云图数据，基于云采样位置信息，对目标层级云图数据进行采样，得到第一采样点的采样云信息，如获取目标层级云图数据在云采样位置信息处的像素点的第二像素信息，将该第二像素信息确定为第一采样点的采样云信息。
在一些实施例中，步骤S303还可以通过如下方式实现：当第一视点位置的纬度为第三纬度阈值时，基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的第一云信息；基于云采样位置信息，对极圈云图数据进行采样，得到第一采样点的第二云信息；对第一云信息与第二云信息进行融合处理，得到第一采样点的采样云信息。其中，该第三纬度阈值可以是中低纬度与高纬度的交界。例如，获取极圈云图数据在云采样位置信息处的像素点的第四像素信息，并将第四像素信息确定为第一采样点的第二云信息；对第一云信息与第二云信息进行融合处理，得到第一采样点的采样云信息。
在实际应用中，在得到第一采样点的采样云信息后，可以基于第一采样点的采样云信息，对第一采样点进行渲染；或者，可以为第一采样点的采样云信息添加噪声数据，得到第一采 样点的云密度信息，采用第一采样点的云密度信息，对第一采样点进行渲染。其中，该噪声数据可以是随机噪声，使得在基础云图数据基础上，添加噪声数据，可以提高不同采样点的云密度信息的随机性，却不会破坏云密度信息的有序性，从而可以提高云图处理的准确性。具体的，可以参见下述图9的步骤S907中的相关描述。
在本申请实施例中，可以构建空间坐标系，在空间坐标系中构建基础云图数据；以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系，获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息；不同的视点位置所对应的局部坐标系不同；基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。通过以上过程，可以构建一个基础云图数据，然后在进行采样时，是基于局部坐标系进行的坐标确定，也就是说，相当于把“在球体表面生成球形贴图”的问题，转化为“球体在平面上进行滚动，并生成切点附近的平面贴图(即局部坐标系的采样)”的问题，简化了云图数据的制作流程与效率，而且，基于局部采样，使得在生成精度较高的云图数据时，也无需耗费较多的开销和资源等，从而节省资源，提高云图处理的准确性及效率。
接下来参见图9，图9是本申请实施例提供的一种云图数据迭代处理流程示意图。如图9所示，该过程可以包括如下步骤：
步骤S901，构建空间坐标系，在空间坐标系中构建基础云图数据。
在实际应用中，可以参见图3的步骤S301中的相关描述，在此不再进行赘述。
步骤S902，在第i个视频移动帧中，以第一视点位置i为原点，构建第一视点位置i所对应的局部坐标系i。
在实际应用中，第一视点位置i所对应的局部坐标系，是指以第一视点位置i为原点，由第一视点位置i所构建的三个坐标轴组成的坐标系，具体可以参见图3的步骤S302中的相关描述，在此不再进行赘述。
步骤S903，获取第一采样点，获取第一采样点在第一视点位置i所对应的局部坐标系i中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息。
在实际应用中，计算机设备可以基于第一视点位置i，获取第一采样点，该第一采样点的数量为一个或至少两个，其中，第一采样点是指以第一视点位置i所对应的视点采集设备所覆盖的区域中的视点。或者，可以获取包括第一视点位置i所在的视点区域，将该视点区域所包括的视点确定为第一采样点，其中，视点区域的尺寸可以是局部采样尺寸，该局部采样尺寸可以是默认的尺寸，也可以是根据相邻的视点移动帧分别对应的第一视点位置之间的距离所确定的等，在此不做限制。
步骤S904，基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。
在实际应用中，可以参见图3的步骤S302中的相关描述，在此不再进行赘述。即，可以从基础云图数据中获取云采样位置信息处的第一像素信息，将第一像素信息确定为第一采样点的采样云信息；或者，在基础云图数据包括N个层级云图数据时，从确定的第一层级云图数据中，获取云采样位置信息处的第二像素信息，将第二像素信息确定为第一采样点的采样云信息等。该采样云信息可以表示对应的第一采样点处的云的浓度等信息，可以用于构建云图数据。
步骤S905，检测云图数据完成情况。
在实际应用中，可以检测对象模型的云图数据完成情况，若对象模型的云图处理完成，则执行步骤S907；若对象模型的云图处理未完成，则执行步骤S906。
步骤S906，i++，对对象模型进行模拟移动，确定第一视点位置i。
在实际应用中，i++，用于进行下一个视点移动帧，如，假定对i进行更新是i＝2+1，也就是由第二个视点移动帧更新为第三个视点移动帧，此时，相当于第i个视点移动帧为第三个视点移动帧，第二个视点移动帧变为第(i-1)个视点移动帧，第(i-1)个视点移动帧所对 应的第一视点位置记作第一视点位置(i-1)。可以对对象模型进行模拟移动，确定移动后的对象模型与模拟平面之间的切点，将移动后的对象模型与模拟平面之间的切点，确定为第一视点位置i；或者，可以在进行i++后，在待解析对象的对象模型的表面，确定以第一视点位置(i--)为中心，以相邻移动阈值为半径的候选位置范围，在候选位置范围内随机选取第一视点位置i；或者，沿着视点移动方向，在候选位置范围内选取第一视点位置i，该视点移动方向用于表示第一视点位置i相对于第一视点位置(i--)的方向。其中，该第一视点位置i的局部坐标系在待解析对象的对象表面所覆盖的区域，与第一视点位置(i--)的局部坐标系在待解析对象的对象模型的表面所覆盖的区域邻接，或者相交的区域小于或等于局部重复阈值。
进一步返回执行步骤S902。其中，第一视点位置i与第一视点位置(i-1)之间的距离小于或等于相邻帧移动阈值，该相邻帧移动阈值远小于对象模型的半径，也就是任意两个相邻的视点移动帧所对应的第一视点位置之间的距离，小于或等于相邻帧移动阈值。通过这一方式，使得在构建局部坐标系时，相邻两个视点移动帧分别对应的局部坐标系变化较小，即第一坐标轴的方向和第三坐标轴的方向不至于发生突变，而是在人眼无法察觉的范围渐变，从而使得人眼对云图数据采样的观测结果是符合连续相对运动规律的，提高云图处理的准确性。
步骤S907，基于第一采样点的采样云信息进行云图数据渲染。
在本申请实施例中，通过以上过程，可以获取到所有第一采样点的采样云信息。假定所有第一采样点的数量为M，M为正整数。其中，由于云图数据用于表示俯视视角下，体积云的分布及形态等信息，也就是说，可以将M个第一采样点分别对应的采样云信息进行组合，得到对象模型所对应的对象云图数据。或者，可以对M个第一采样点分别对应的采样云信息添加噪声数据，得到M个第一采样点分别对应的云密度信息。基于M个第一采样点分别对应的云密度信息，对M个第一采样点进行渲染，得到对象模型所对应的对象云图数据。也就是说，已知了各个第一采样点所对应的云的相关信息(即采样云信息或云密度信息)，即可以将各个第一采样点所对应的云的相关信息，构建成为对象模型的对象云图数据，从而实现对待解析对象的云图数据绘制。
其中，该对象模型为地球的模型时，可以将该对象云图数据集成至航行模拟系统中，在航行模拟系统中进行模拟飞行时，可以渲染对象云图数据，以模拟真实天空场景，使得可以在对象云图数据基础上，模拟真实的飞行场景。或者，可以将对象云图数据集成至虚拟现实(Virtual Reality，VR)场景中，在VR场景中渲染对象云图数据，使得参与该VR场景的用户可以获取到更为真实的场景体验，包括航行VR场景或VR游戏场景等，例如，在航行VR场景中，用户可以基于渲染的对象云图数据，体验真实的航行过程；如在VR游戏场景中，可以在需要云图数据时，基于对象云图数据渲染VR游戏场景，用户可以参与该VR游戏场景，如空战类VR游戏等。
通过以上过程，只需要提供一个较小的正方形区域内的平面云图数据，即初始单位云图数据，就可以在运行时动态生成全球范围内无缝衔接的球面云图数据采样，可以节省大量精细存储云图数据的空间及云图处理资源，提高云图处理的效率及准确性。
接下来参见图10，图10是本申请实施例提供的一种云采样流程示意图。如图10所示，该过程可以包括如下步骤：
步骤S1001，获取第一视点位置的纬度，基于第一视点位置的纬度，构建第一视点位置所对应的局部坐标系。
在实际应用中，获取基础云图数据及极圈云图数据。具体的，可以参见图3的步骤S301所示，生成基础云图数据。在对象模型的极点位置处，以极点作为原点，以0°和90°经线分别作为横坐标轴及纵坐标轴，构建极点坐标系，针对极点坐标系构建极圈云图数据。
在一些实施例中，可以获取第一视点位置的纬度，当第一视点位置的纬度小于或等于第一纬度阈值，也就是第一视点位置位于中低纬度地区时，则将第一采样点的经纬度坐标，确定为第一采样点的云采样位置信息；或者，对第一采样点的经纬度坐标进行取余处理，得到第一采样点的云采样位置信息，执行步骤S1003，将基础云图数据确定为待采样云图数据， 基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息的步骤。当第一视点位置的纬度大于第一纬度阈值时，则执行以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系的过程，进一步执行步骤S1002。
在另一些实施例中，当第一视点位置的纬度大于第二纬度阈值，表示第一视点位置位于高纬度地区，则在对象模型的极点处构建极点坐标系，在极点坐标系中获取第一采样点的云采样位置信息，将极圈云图数据确定为基础云图数据，也就是将极圈云图数据确定为待采样云图数据，执行步骤S1003，基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息的过程；对象模型是指进行云图解析的模型。若第一视点位置小于第二纬度阈值，则执行构建空间坐标系，在空间坐标系中构建基础云图数据的过程，进一步，将基础云图数据确定为待采样云图数据，执行步骤S1002。
其中，第一纬度阈值与第二纬度阈值可以相同，此时，第一纬度阈值与第二纬度阈值为中低纬度区域和高纬度区域的界线，也就是此时可以为第三纬度阈值，第一纬度阈值与第二纬度阈值也可以不同。
在一些实施例中若第一视点位置的纬度小于或等于第一纬度阈值，则将第一采样点的经纬度坐标，确定为第一采样点的云采样位置信息，将基础云图数据确定为待采样云图数据，执行步骤S1003；若第一视点位置的纬度大于第二纬度阈值，则在对象模型的极点处构建极点坐标系，在极点坐标系中获取第一采样点的云采样位置信息，将极圈云图数据确定为待采样云图数据，执行步骤S1003。若第一视点位置的纬度大于第一纬度阈值，且小于或等于第二纬度阈值，则将基础云图数据确定为待采样云图数据，执行步骤1002。
在另一些实施例中，若第一视点位置的纬度为第三纬度阈值，则将基础云图数据及极圈云图数据均确定为待采样云图数据，执行步骤S1002。
步骤S1002，获取第一采样点在第一视点位置所对应的局部坐标系中的云采样位置信息。
在本申请实施例中，具体可以参见图3的步骤S302的相关描述，在此不做限制。
步骤S1003，获取待采样云图数据，基于云采样位置信息对待采样云图数据进行采样，得到第一采样点的采样云信息。
在本申请实施例中，具体可以参见图3的步骤S303的相关描述，在此不做限制。例如，在待采样云图数据包括基础云图数据及极圈云图数据时，可以基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的第一云信息；基于云采样位置信息，对极圈云图数据进行采样，得到第一采样点的第二云信息；对第一云信息与第二云信息进行融合处理，得到第一采样点的采样云信息。
通过第一视点位置的纬度，进行针对性采样处理，使得在特殊情况下，可以实现云图数据的特殊处理，从而可以使得云图数据的采样结果可以无缝平滑过渡，如，在第一视点位置的纬度为第三纬度阈值，则将基础云图数据及极圈云图数据均确定为待采样云图数据，对两侧云图数据进行一定的采样融合操作，从而使得云图数据的采样结果可以无缝平滑过渡，提高云图处理的准确性。
参见图11，图11是本申请实施例提供的一种云图处理装置示意图，该云图处理装置可以是运行于计算机设备中的一个计算机程序(包括程序代码等)，例如该云图处理装置可以为一个应用软件；该装置可以用于执行本申请实施例提供的方法中的相应步骤。如图11所示，该云图处理装置1100可以用于图3所对应实施例中的计算机设备，具体的，该装置可以包括：云图构建模块11、局部构建模块12、位置确定模块13及云采样模块14。
云图构建模块11，配置为构建空间坐标系，在空间坐标系中构建基础云图数据；
局部构建模块12，配置为以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系；不同的视点位置所对应的局部坐标系不同；
位置确定模块13，配置为获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息；
云采样模块14，配置为基于云采样位置信息，对基础云图数据进行采样，得到第一采样 点的采样云信息。
在一些实施例中，云图构建模块11，还配置为获取候选云图数据，并将所述候选云图数据与所述空间坐标系进行关联，生成初始单位云图数据；将所述初始单位云图数据确定为基础云图数据，或者，对所述初始单位云图数据进行无缝延续生成基础云图数据。
在一些实施例中，云图构建模块11，包括：
图像转换单元111，配置为获取待解析图像，对待解析图像的图像尺寸进行坐标转换，得到空间坐标系；
解析映射单元112，配置为对待解析图像进行图像解析，将解析结果映射至空间坐标系，得到初始单位云图数据；
云图延续单元113，配置为在初始单位云图数据基础上，对初始单位云图数据进行无缝延续，生成基础云图数据；基础云图数据是指无限大的云图数据。
在一些实施例中，待解析图像的数量为N，N为正整数；该云图延续单元113，包括：
层级生成子单元1131，配置为在N个待解析图像分别对应的初始单位云图数据基础上，对N个初始单位云图数据分别进行无缝延续，生成N个层级云图数据；
高度确定子单元1132，配置为基于N个待解析图像分别对应的高度范围，确定N个层级云图数据分别对应的云图高度范围；
层级组合子单元1133，配置为基于N个层级云图数据分别对应的云图高度范围，对N个层级云图数据进行组合，得到基础云图数据。
在一些实施例中，该局部构建模块12，包括：
第一构建单元121，配置为在第i个视点移动帧中，当i为初始值时，基于第一地理坐标点确定第一视点位置i，基于第二地理坐标点与第一地理坐标点确定第一坐标轴i，以对象模型中心点至第一视点位置i的方向构建第二坐标轴i，基于第一坐标轴i及第二坐标轴i确定第三坐标轴i，将第一坐标轴i、第二坐标轴i及第三坐标轴i，组成第一视点位置i所对应的局部坐标系i；对象模型中心点是指进行云图解析的对象模型的中心点；
第二构建单元122，配置为当i不为初始值时，将第i个视点移动帧所处的视角采集点确定为第一视点位置i，以对象模型中心点至第一视点位置i的方向构建第二坐标轴i，基于第(i-1)个视点移动帧所对应的第三坐标轴(i-1)及第二坐标轴i，构建第一坐标轴i，基于第一坐标轴i及第二坐标轴i，构建第三坐标轴i，将第一坐标轴i、第二坐标轴i及第三坐标轴i，组成第一视点位置i所对应的局部坐标系i。
在一些实施例中，在基于第二地理坐标点与第一地理坐标点确定第一坐标轴i时，该第一构建单元121包括：
坐标投影子单元1211，配置为获取对象模型在第一地理坐标点处的切平面，获取第二地理坐标点在切平面中的投影长度；
坐标轴确定子单元1212，配置为当第二地理坐标点在所述切平面中的投影长度不为0，将第一地理坐标点至第二地理坐标点的方向确定为第一坐标轴i；当第二地理坐标点在切平面中的投影长度为0时，对第二地理坐标点沿所述切平面进行移动，得到第三地理坐标，当第三地理坐标在切平面上的投影长度不为0时，将第一地理坐标点至第三地理坐标的方向确定为第一坐标轴i。
在一些实施例中，该位置确定模块13，包括：
位置获取单元131，配置为获取第一视点位置的位置信息；
偏移获取单元132，配置为获取第一采样点在第一视点位置所对应的局部坐标系中的坐标偏移量；
位置确定单元133，配置为在第一视点位置的位置信息中添加坐标偏移量，确定第一采样点的采样点位置信息；
位置转换单元134，配置为对采样点位置信息进行取余处理，得到云采样位置信息。
其中，基础云图数据包括N个层级云图数据；该云采样模块14，包括：
层级确定单元141，配置为从N个层级云图数据中，获取云图高度范围包括云采样位置信息中的位置高度的目标层级云图数据；
层级采样单元142，配置为基于云采样位置信息，对目标层级云图数据进行采样，得到第一采样点的采样云信息。
在一些实施例中，该装置1100还包括：
采样调用模块15，配置为当第一视点位置的纬度小于或等于第一纬度阈值时，将第一采样点的经纬度坐标，确定为第一采样点的云采样位置信息，执行基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息的步骤。
局部调用模块16，配置为当第一视点位置的纬度大于第一纬度阈值时，执行以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系的过程。
在一些实施例中，该装置1100还包括：
极点处理模块17，配置为若第一视点位置的纬度大于第二纬度阈值，则在对象模型的极点处构建极点坐标系，在极点坐标系中获取第一采样点的云采样位置信息，将极圈云图数据确定为基础云图数据，执行基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息的过程；对象模型是指进行云图解析的模型；
云图调用模块18，配置为若第一视点位置小于第二纬度阈值，则执行构建空间坐标系，在空间坐标系中构建基础云图数据的过程。
在一些实施例中，该云采样模块14，包括：
第一采样单元143，配置为若第一视点位置的纬度为第三纬度阈值，则基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的第一云信息；
第二采样单元144，配置为基于云采样位置信息，对极圈云图数据进行采样，得到第一采样点的第二云信息；
采样融合单元145，配置为对第一云信息与第二云信息进行融合处理，得到第一采样点的采样云信息。
在一些实施例中，该装置1100还包括：
信息处理模块19，配置为为第一采样点的采样云信息添加噪声数据，得到第一采样点的云密度信息；
采样渲染模块20，配置为采用第一采样点的云密度信息，对第一采样点进行渲染。
本申请实施例提供了一种云图处理装置，该装置可以构建空间坐标系，在空间坐标系中构建基础云图数据；以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系，获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息；不同的视点位置所对应的局部坐标系不同；基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。通过以上过程，可以构建一个基础云图数据，然后在进行采样时，是基于局部坐标系进行的坐标确定，也就是说，相当于把“在球体表面生成球形贴图”的问题，转化为“球体在平面上进行滚动，并生成切点附近的平面贴图(即局部坐标系的采样)”的问题，简化了云图数据的制作流程与效率，而且，基于局部采样，使得在生成精度较高的云图数据时，也无需耗费较多的开销和资源等，从而节省资源，提高云图处理的准确性及效率。
参见图12，图12是本申请实施例提供的一种计算机设备的结构示意图。如图12所示，本申请实施例中的计算机设备可以包括：一个或多个处理器1201、存储器1202和输入输出接口1203。该处理器1201、存储器1202和输入输出接口1203通过总线1204连接。存储器1202用于存储计算机程序，该计算机程序包括程序指令，输入输出接口1203用于接收数据及输出数据，如用于计算机设备与业务设备之间进行数据交互；处理器1201用于执行存储器1202存储的程序指令。
在一些实施例中，该处理器1201可以执行如下操作：
构建空间坐标系，在空间坐标系中构建基础云图数据；以第一视点位置作为原点，构建 第一视点位置应的局部坐标系，获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息；不同的视点位置所对应的局部坐标系不同；基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。
在一些实施例中，该处理器1201可以是中央处理单元(central processing unit，CPU)，该处理器还可以是其他通用处理器、数字信号处理器(digital signal processor，DSP)、专用集成电路(application specific integrated circuit，ASIC)、现成可编程门阵列(field-programmable gate array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
该存储器1202可以包括只读存储器和随机存取存储器，并向处理器1201和输入输出接口1203提供指令和数据。存储器1202的一部分还可以包括非易失性随机存取存储器。例如，存储器1202还可以存储设备类型的信息。
具体实现中，该计算机设备可通过其内置的各个功能模块执行如该图3中各个步骤所提供的实现方式，具体可参见该图3中各个步骤所提供的实现方式，在此不再赘述。
本申请实施例通过提供一种计算机设备，包括：处理器、输入输出接口、存储器，通过处理器获取存储器中的计算机程序，执行该图3中所示方法的各个步骤，进行云图处理操作。本申请实施例实现了可以构建空间坐标系，在空间坐标系中构建基础云图数据；以第一视点位置作为原点，构建第一视点位置所对应的局部坐标系，获取第一采样点在第一视点位置所对应的局部坐标系中的采样点位置信息，对采样点位置信息进行位置转换，得到云采样位置信息；不同的视点位置所对应的局部坐标系不同；基于云采样位置信息，对基础云图数据进行采样，得到第一采样点的采样云信息。通过以上过程，可以构建一个基础云图数据，然后在进行采样时，是基于局部坐标系进行的坐标确定，也就是说，相当于把“在球体表面生成球形贴图”的问题，转化为“球体在平面上进行滚动，并生成切点附近的平面贴图(即局部坐标系的采样)”的问题，简化了云图数据的制作流程与效率，而且，基于局部采样，使得在生成精度较高的云图数据时，也无需耗费较多的开销和资源等，从而节省资源，提高云图处理的准确性及效率。
本申请实施例还提供一种计算机可读存储介质，该计算机可读存储介质存储有计算机程序，该计算机程序适于由该处理器加载并执行图3中各个步骤所提供的云图处理方法，具体可参见该图3中各个步骤所提供的实现方式，在此不再赘述。另外，对采用相同方法的有益效果描述，也不再进行赘述。对于本申请所涉及的计算机可读存储介质实施例中未披露的技术细节，请参照本申请方法实施例的描述。作为示例，计算机程序可被部署为在一个计算机设备上执行，或者在位于一个地点的多个计算机设备上执行，又或者，在分布在多个地点且通过通信网络互连的多个计算机设备上执行。
该计算机可读存储介质可以是前述任一实施例提供的云图处理装置或者该计算机设备的内部存储单元，例如计算机设备的硬盘或内存。该计算机可读存储介质也可以是该计算机设备的外部存储设备，例如该计算机设备上配备的插接式硬盘，智能存储卡(smart media card，SMC)，安全数字(secure digital，SD)卡，闪存卡(flash card)等。进一步地，该计算机可读存储介质还可以既包括该计算机设备的内部存储单元也包括外部存储设备。该计算机可读存储介质用于存储该计算机程序以及该计算机设备所需的其他程序和数据。该计算机可读存储介质还可以用于暂时地存储已经输出或者将要输出的数据。
本申请实施例还提供了一种计算机程序产品或计算机程序，该计算机程序产品或计算机程序包括计算机指令，该计算机指令存储在计算机可读存储介质中。计算机设备的处理器从计算机可读存储介质读取该计算机指令，处理器执行该计算机指令，使得该计算机设备执行图3中的各种可选方式中所提供的方法，实现了构建一个基础云图数据，然后在进行采样时，是基于局部坐标系进行的坐标确定，也就是说，相当于把“在球体表面生成球形贴图”的问题，转化为“球体在平面上进行滚动，并生成切点附近的平面贴图(即局部坐标系的采样)” 的问题，简化了云图数据的制作流程与效率，而且，基于局部采样，使得在生成精度较高的云图数据时，也无需耗费较多的开销和资源等，从而节省资源，提高云图处理的准确性及效率。
本申请实施例的说明书和权利要求书及附图中的术语“第一”、“第二”等是用于区别不同对象，而非用于描述特定顺序。此外，术语“包括”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、装置、产品或设备没有限定于已列出的步骤或模块，而是可选地还包括没有列出的步骤或模块，或可选地还包括对于这些过程、方法、装置、产品或设备固有的其他步骤单元。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在该说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
本申请实施例提供的方法及相关装置是参照本申请实施例提供的方法流程图和/或结构示意图来描述的，具体可由计算机程序指令实现方法流程图和/或结构示意图的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。这些计算机程序指令可提供到通用计算机、专用计算机、嵌入式处理机或其他可编程云图处理设备的处理器以产生一个机器，使得通过计算机或其他可编程云图处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或结构示意图一个方框或多个方框中指定的功能的装置。这些计算机程序指令也可存储在能引导计算机或其他可编程云图处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或结构示意图一个方框或多个方框中指定的功能。这些计算机程序指令也可装载到计算机或其他可编程云图处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或结构示意一个方框或多个方框中指定的功能的步骤。
本申请实施例方法中的步骤可以根据实际需要进行顺序调整、合并和删减。
本申请实施例装置中的模块可以根据实际需要进行合并、划分和删减。
以上所揭露的仅为本申请较佳实施例而已，当然不能以此来限定本申请之权利范围，因此依本申请权利要求所作的等同变化，仍属本申请所涵盖的范围。

Claims (18)

  1. 一种云图处理方法，所述方法由计算机设备执行，所述方法包括：
    构建空间坐标系，在所述空间坐标系中构建基础云图数据；
    以第一视点位置作为原点，构建所述第一视点位置所对应的局部坐标系，不同的视点位置所对应的局部坐标系不同；
    获取第一采样点在所述第一视点位置所对应的局部坐标系中的采样点位置信息；
    对所述采样点位置信息进行位置转换，得到云采样位置信息；
    基于所述云采样位置信息，对所述基础云图数据进行采样，得到所述第一采样点的采样云信息。
  2. 如权利要求1所述的方法，其中，所述在所述空间坐标系中构建基础云图数据，包括：
    获取候选云图数据，并将所述候选云图数据与所述空间坐标系进行关联，生成初始单位云图数据；
    将所述初始单位云图数据确定为基础云图数据，或者，对所述初始单位云图数据进行无缝延续生成基础云图数据。
  3. 如权利要求1所述的方法，其中，所述构建空间坐标系，在所述空间坐标系中构建基础云图数据，包括：
    获取待解析图像，对所述待解析图像的图像尺寸进行坐标转换，得到空间坐标系；
    对所述待解析图像进行图像解析，将解析结果映射至所述空间坐标系，得到初始单位云图数据；
    在所述初始单位云图数据基础上，对所述初始单位云图数据进行无缝延续，生成基础云图数据；所述基础云图数据是指无限大的云图数据。
  4. 如权利要求3所述的方法，其中，所述待解析图像的数量为N，N为正整数；所述在所述初始单位云图数据基础上，对所述初始单位云图数据进行无缝延续，生成基础云图数据，包括：
    在N个待解析图像分别对应的初始单位云图数据基础上，对N个初始单位云图数据分别进行无缝延续，生成N个层级云图数据；
    基于所述N个待解析图像分别对应的高度范围，确定所述N个层级云图数据分别对应的云图高度范围；
    基于所述N个层级云图数据分别对应的云图高度范围，对所述N个层级云图数据进行组合，得到基础云图数据。
  5. 如权利要求1所述的方法，其中，以第一视点位置作为原点，构建所述第一视点位置所对应的局部坐标系，包括：
    在第i个视点移动帧中，当i为初始值时，基于第一地理坐标点确定第一视点位置i，基于第二地理坐标点与所述第一地理坐标点确定第一坐标轴i，以对象模型中心点至所述第一视点位置i的方向构建第二坐标轴i，基于所述第一坐标轴i及所述第二坐标轴i确定第三坐标轴i，将所述第一坐标轴i、所述第二坐标轴i及所述第三坐标轴i，组成所述第一视点位置i所对应的局部坐标系i；所述对象模型中心点是指进行云图解析的对象模型的中心点；
    当i不为所述初始值时，将所述第i个视点移动帧所处的视角采集点确定为第一视点位置i，以所述对象模型中心点至所述第一视点位置i的方向构建第二坐标轴i，基于第(i-1)个视点移动帧所对应的第三坐标轴(i-1)及所述第二坐标轴i，构建第一坐标轴i，基于所述第一坐标轴i及所述第二坐标轴i，构建第三坐标轴i，将所述第一坐标轴i、所述第二坐标轴i及所述第三坐标轴i，组成所述第一视点位置i所对应的局部坐标系i。
  6. 如权利要求5所述的方法，其中，所述基于第二地理坐标点与所述第一地理坐标点确定第一坐标轴i，包括：
    获取所述对象模型在所述第一地理坐标点处的切平面，并获取第二地理坐标点在所述切 平面中的投影长度；
    当所述第二地理坐标点在所述切平面中的投影长度不为0时，将所述第一地理坐标点至所述第二地理坐标点的方向确定为第一坐标轴i。
  7. 如权利要求6所述的方法，其中，所述基于第二地理坐标点与所述第一地理坐标点确定第一坐标轴i，包括：
    当所述第二地理坐标点在所述切平面中的投影长度为0时，对所述第二地理坐标点沿所述切平面进行移动，得到第三地理坐标；
    当所述第三地理坐标在所述切平面上的投影长度不为0时，将所述第一地理坐标点至所述第三地理坐标的方向确定为第一坐标轴i。
  8. 如权利要求1所述的方法，其中，所述获取第一采样点在所述第一视点位置所对应的局部坐标系中的采样点位置信息，对所述采样点位置信息进行位置转换，得到云采样位置信息，包括：
    获取所述第一视点位置的位置信息，获取所述第一采样点在所述第一视点位置所对应的局部坐标系中的坐标偏移量；
    在所述第一视点位置的位置信息中添加所述坐标偏移量，确定所述第一采样点的采样点位置信息；
    对所述采样点位置信息进行取余处理，得到云采样位置信息。
  9. 如权利要求1所述的方法，其中，所述基础云图数据包括N个层级云图数据；所述基于所述云采样位置信息，对所述基础云图数据进行采样，得到所述第一采样点的采样云信息，包括：
    从所述N个层级云图数据中，获取云图高度范围包括所述云采样位置信息中的位置高度的目标层级云图数据；
    基于所述云采样位置信息，对所述目标层级云图数据进行采样，得到所述第一采样点的采样云信息。
  10. 如权利要求1所述的方法，其中，所述基于所述云采样位置信息，对所述基础云图数据进行采样，得到所述第一采样点的采样云信息，包括：
    获取所述基础云图数据在所述云采样位置信息处的像素点的第一像素信息，并将所述第一像素信息确定为所述第一采样点的采样云信息。
  11. 如权利要求1所述的方法，其中，所述方法还包括：
    当所述第一视点位置的纬度小于或等于第一纬度阈值时，将所述第一采样点的经纬度坐标，确定为所述第一采样点的云采样位置信息，执行所述基于所述云采样位置信息，对所述基础云图数据进行采样，得到所述第一采样点的采样云信息的步骤；
    当所述第一视点位置的纬度大于所述第一纬度阈值时，执行所述以第一视点位置作为原点，构建所述第一视点位置所对应的局部坐标系的过程。
  12. 如权利要求1所述的方法，其中，所述方法还包括：
    当所述第一视点位置的纬度大于第二纬度阈值时，在对象模型的极点处构建极点坐标系，在所述极点坐标系中获取所述第一采样点的云采样位置信息，将极圈云图数据确定为基础云图数据，执行所述基于所述云采样位置信息，对所述基础云图数据进行采样，得到所述第一采样点的采样云信息的过程；所述对象模型是指进行云图解析的模型；
    当所述第一视点位置小于所述第二纬度阈值时，执行所述构建空间坐标系，在所述空间坐标系中构建基础云图数据的过程。
  13. 如权利要求1所述的方法，其中，所述基于所述云采样位置信息，对所述基础云图数据进行采样，得到所述第一采样点的采样云信息，包括：
    当所述第一视点位置的纬度为第三纬度阈值时，基于所述云采样位置信息，对所述基础云图数据进行采样，得到所述第一采样点的第一云信息；
    基于所述云采样位置信息，对极圈云图数据进行采样，得到所述第一采样点的第二云信 息；
    对所述第一云信息与所述第二云信息进行融合处理，得到所述第一采样点的采样云信息。
  14. 如权利要求1所述的方法，其中，所述方法还包括：
    为所述第一采样点的采样云信息添加噪声数据，得到所述第一采样点的云密度信息；
    采用所述第一采样点的云密度信息，对所述第一采样点进行渲染。
  15. 一种云图处理装置，所述装置包括：
    云图构建模块，配置为构建空间坐标系，在所述空间坐标系中构建基础云图数据；
    局部构建模块，配置为以第一视点位置作为原点，构建所述第一视点位置所对应的局部坐标系；
    位置确定模块，配置为获取第一采样点在所述第一视点位置所对应的局部坐标系中的采样点位置信息，对所述采样点位置信息进行位置转换，得到云采样位置信息；不同的视点位置所对应的局部坐标系不同；
    云采样模块，配置为基于所述云采样位置信息，对所述基础云图数据进行采样，得到所述第一采样点的采样云信息。
  16. 一种计算机设备，包括处理器、存储器、输入输出接口；
    所述处理器分别与所述存储器和所述输入输出接口相连，其中，所述输入输出接口用于接收数据及输出数据，所述存储器用于存储计算机程序，所述处理器用于调用所述计算机程序，以使得所述计算机设备执行权利要求1-14任一项所述的方法。
  17. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序适于由处理器加载并执行，以使得具有所述处理器的计算机设备执行权利要求1-14任一项所述的方法。
  18. 一种计算机程序产品，所述计算机程序产品包括计算机程序，所述计算机程序被处理器执行时实现如权利要求1-14中任一项所述的方法。
PCT/CN2024/084465 2023-05-26 2024-03-28 云图处理方法、装置、计算机设备、计算机可读存储介质及计算机程序产品 Ceased WO2024244659A1 (zh)

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