Classifications

• • G—PHYSICS
  • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
  • G03B—APPARATUS OR ARRANGEMENTS FOR TAKING PHOTOGRAPHS OR FOR PROJECTING OR VIEWING THEM; APPARATUS OR ARRANGEMENTS EMPLOYING ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ACCESSORIES THEREFOR
  • G03B15/00—Special procedures for taking photographs; Apparatus therefor
• • 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
  • G06T7/00—Image analysis
  • G06T7/50—Depth or shape recovery
  • G06T7/507—Depth or shape recovery from shading
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2200/00—Indexing scheme for image data processing or generation, in general
  • G06T2200/08—Indexing scheme for image data processing or generation, in general involving all processing steps from image acquisition to 3D model generation
• • 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
  • G06T2207/10—Image acquisition modality
  • G06T2207/10032—Satellite or aerial image; Remote sensing

Definitions

• the present invention relates to a method of automatic reconstruction of three-dimensional models of building roofs superstructures and the creation of model or footprint of buildings from these same roofs, and this from unique aerial or satellite images, different positional parameters of the camera and the sun captured during the shooting, as well as from a device for implementing said method. Tracks of algorithms are provided to implement said method.
• the reconstruction of 3D models of buildings is generally established from stereoscopic images, other methods using for example the LIDAR (laser / radar concept) or SAR also produce a certain interest but are even more expensive to implement and only allow to reproduce the geometry of buildings at best to a precision of one meter.
• LIDAR laser / radar concept
• SAR also produce a certain interest but are even more expensive to implement and only allow to reproduce the geometry of buildings at best to a precision of one meter.
• Stereoscopic methods have the merit of being self-sufficient by joining the image to geometry and are therefore more affordable, which is why they represent the traditional methods of producing geometry.
• Segment / edge matching can not work properly on medium-resolution images as is often the case with aerial or satellite imagery (see “Using Shadows in Finding Surface Orientations” Shafer & Kanade 82; -sens sentences “Huffman, 1971,” on things “Clowes, 1971).
• the proposed method based on superstructures / details roofing makes it possible to work on entire shadows in an almost systematic way and to obtain reproducible results on a large scale, it also works in an automated way thanks to the use of forms recognition and optimization methods as well as by the use of scores or methods of a Bayesian nature mapped to a method of projecting a 3D object into the plane of the snapshot that uses and adjusts the slope of the underlying support.
• the method according to the invention consists of a series of treatments allowing the automated reconstruction of three-dimensional models of superstructures / details of the roofs of buildings and the creation of three-dimensional models / or footprints of buildings from these same roofs, these treatment sequences being applied from one or more aerial or satellite images possibly assisted by a digital model of elevation or surface and from the positional parameters of the camera and the sun captured during the shooting, these treatments consisting of: a step of recognizing the shapes and / or primitives of roof details associated with the recognition of the shapes and / or primitives of the shadows of these same details.
• this reconstruction phase is supported by a parameter optimization phase of said 3D model, this optimization phase then also measuring its efficiency via the said projection of said 3D model at each parameter change.
• the parameters of the 3D model to optimizing referred are in particular the height and the slope of the support which are related parameters and sensitive to the error, but can be also each of the other parameters of the 3D model.
• the topological properties used are the length of
• the pattern recognition uses a hierarchical graph of regions of the photograph in such a way that more or less fine details can be detected and cut off and that the region which represents them can be contained in a detail including - so topologically in a bounding region associated -, from the models of details of the roof, the Slope values are associated with each detail of the roof.
• Each of the roof details is then classified into a new set of regions by similarity of the slope values and the relatedness of the details of the roof in question to the other details of the roof.
• the pairs of related regions of opposite average slope value are determined as representing a symmetrical roof (this is ie, with two opposite slopes): already having roof boundaries, which are considered to be the closest shape / region encompassing the roof details of the pair of related classes / regions considered, the boundaries of each roof panel represent half of this region.
• the demarcation orientation of the halves is easily obtained from the centers of gravity / centroid of each set of roof details of the pair of regions considered. -from the footprint of the roofs, the height of a model of frame can be found by proceeding by progressive changes in the height of the 3D model, by the projection of the latter on the plane of the cliche and the addition of a score that is a function of the degree of similarity of the 3D model projected to the original shot, then by the optimization of this score.
• Fig. 1 Enlargement of the image to be treated on a detail of the roof (chimney).
• Fig. 2 Creating regions by pattern recognition, ranking regions in a hierarchical graph.
• Fig. 3 Creation of a basic 3D model of roof detail from recognized regions in FIG. 2.
• Fig. 4 Change the parameters of the 3D detail model of the roof and projection.
• Fig. 5 Determination of slopes and 3D models of roof sections from 3D models of roof details and extrusion of a 3D building model from an optimized roof footprint.
• the method consists of a step of detecting the details of the roofs (2) by the use of a hierarchical graph (5) of segmentations in regions of the image to be processed (1) (see FIG. 1 and 2).
• a basic 3D model (7) of the considered roof detail (2) is established: height of the roof detail (2) being determined from the length of the shadow (3) for a slope (9) by default or estimated directly if the orientation of the shadow allows (see Fig.3).
• an optimization phase is carried out for each of the roof details (2), the 3D parameters such as the height, the width, the length and the two angles that determine the slope (9) of the roof panel (11). on which the detail (2) is located varies and the variation of the model in question (7) is established with each change of parameters making it possible to establish a projection of the 3D model in "wire” (8) corresponding and a projection of its shadow (10).
• This "wireframe” model (8) is assigned a score corresponding to the compression rate of the image according to the regions delimited by the projections of the 3D "wireframe” (8) plane edges.
• snapshot (6) considered horizontal: the regions within the shapes delimited by each projection of the edges being assigned the average value of the pixels of the region (see Fig.4). From the slope (9) of each roof detail (2), a placing similar roof details (2) and similar slope values (9) in roof sections (11), the opposite roof sections forming a "symmetrical" roof (14).
• the position of the roof edge (12) is determined from the centers of gravity (13) of the roof details (2) of each roof panel (11).
• the footprint of the building supporting the roof (14) can be determined and a building model can be obtained by optimizing the parameters of a 3D model in "wire" (8) (see Fig.5), new roof slope data and roof detail models can also be used to refine a digital surface model.
• the dimensions of the roof details are of the order of one meter.
• the method according to the invention is particularly intended for the reproduction of geolocated virtual universes and the creation of maps and 3D geographic information systems.

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• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Computer Graphics (AREA)
• Geometry (AREA)
• Software Systems (AREA)
• Image Processing (AREA)
• Processing Or Creating Images (AREA)

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• 2009
  • 2009-01-27 FR FR0900355A patent/FR2941542B1/fr not_active Expired - Fee Related
• 2010
  • 2010-01-28 WO PCT/IB2010/050375 patent/WO2010086809A2/fr not_active Ceased
  • 2010-01-28 EP EP10706377A patent/EP2391921A2/de not_active Withdrawn

Non-Patent Citations (1)

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