WO2015149302A1 - Procédé pour reconstruire un modèle d'arbre sur la base d'un nuage de points et d'un entraînement de données - Google Patents

Procédé pour reconstruire un modèle d'arbre sur la base d'un nuage de points et d'un entraînement de données Download PDF

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WO2015149302A1
WO2015149302A1 PCT/CN2014/074634 CN2014074634W WO2015149302A1 WO 2015149302 A1 WO2015149302 A1 WO 2015149302A1 CN 2014074634 W CN2014074634 W CN 2014074634W WO 2015149302 A1 WO2015149302 A1 WO 2015149302A1
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point
tree
point cloud
cylinder
points
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Chinese (zh)
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张晓鹏
李红军
郭建伟
代明睿
刘佳
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Institute of Automation of Chinese Academy of Science
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Institute of Automation of Chinese Academy of Science
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three-dimensional [3D] modelling for computer graphics
    • G06T17/10—Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation

Definitions

  • the present invention relates to the field of cross-technology of plant modeling and computer graphics processing, and relates to physical point measurement using a three-dimensional laser scanner to obtain tree point cloud data, in particular, a scan from A method for reconstructing a complete 3D tree model from 3D point cloud data.
  • Accurate reconstruction of plant models can be applied in many areas, such as directing agricultural forestry production, protecting endangered old trees, or providing virtual environments in digital cinema and entertainment games.
  • the techniques of plant modeling can be roughly divided into four categories: rule-based methods, geometric analysis-based methods, sketch-based methods, and tree-based methods.
  • the reconstruction method based on tree digitization has gained more and more attention in plant reconstruction.
  • tree photographs, point clouds, etc. obtained by tree digitization are mainly used as input data, and some Knowledge and rules are obtained to obtain a plant model similar to the input data.
  • the accuracy of the reconstruction model has gradually become one of the goals of reconstruction.
  • the photo-based reconstruction method has the advantages of convenient data collection and good visual effect of reconstructing the model, but the preparation work is too much, the manual interaction is large, and the reconstruction model and the real model can only remain at a certain angle or a certain angle, and It does not reflect the true form of the plant.
  • the method based on 3D scanning point cloud takes the 3D scan data of the tree as input, and the geometric information is rich, the precision is high, and the model with much higher precision than the photo can be obtained.
  • Cheng2007 Z. Cheng, X. Zhang and B. Chen, "Simple Reconstruction of Tree Branches From a Single Range Image," Journal of Computer Science and Technology, vol. 22, no. 6, pp. 846C858, 2007
  • the crown portion is separated from the trunk portion by manual segmentation, and for the trunk portion, the branch is first detected by the depth of the scanned image.
  • the method can obtain relatively accurate skeleton position and branch radius, but can not reconstruct the twigs and crown.
  • Neubert2007 Nembert, T. Franken and O. Deussen, Approximate Image Based Tree-Modeling Using Particle Flows, ACM Transactions on Graphics (TOG). ACM, 26(3): 88, 2007.
  • particle flow methods for data In the driven tree modeling, they first extract the directional field from the two orthogonally photographed trees, and then use the particle flow method to connect the crown to the main branch along the directional field, so that the reconstructed model is related to the input photo. Match.
  • the present invention uses the motion of a single layer particle stream and is therefore only applicable to secondary structures.
  • the present invention extends the method by using a hierarchical particle flow method to extract a multi-level structure of trees, and the present invention reconstructs a model from point cloud data.
  • Livny2010 Y. Livny, F. Yan, M. Olson, B. Chen, H. Zhang and J. El-Sana, "Automatic Reconstruction of Tree Skeletal Structures from Point Clouds," ACM Trans. Graph, vol.29, no .151, pp. 1C8, 2010.
  • a method based on global fitting optimization is proposed to extract the skeleton structure of point cloud data. This method is more robust, but it still cannot reconstruct the master accurately in the case of severe occlusion.
  • Livny2011 (Y. Livny, S. Pirk, Z. Cheng, F. Yan, O. Deussen, D. Cohen-Or and B. Chen. Texture-lobes for Tree Modelling, ACM Transactions on Graphics (Proceedings of SIGGRAPH 2011), Vol.30, no.4, 2011.)
  • a method for representing trees based on splinters is proposed. The method can be used for reconstruction of trees or for re-presenting existing tree models.
  • the present invention has been carried out on this method.
  • the expansion taking into account the information of the canopy and the visible main branch, and filling the canopy and the main branch by the method of hierarchical particle flow.
  • the present invention provides a tree model reconstruction method based on point cloud and data driving, so as to solve the problem that the existing tree reconstruction method is not accurate, and can only process a relatively simple model, which is difficult for a model with severe occlusion and complex shape. The disadvantage of obtaining better reconstruction results.
  • the present invention provides a point cloud and data driven tree model reconstruction method, the method comprising the following steps: Step S1, obtaining tree point cloud data, pre-processing it, and defining a hierarchical representation of the tree model;
  • Step S2 extracting a main branch skeleton point and a radius thereof from the tree point cloud data, and performing branch and leaf separation processing;
  • Step S3 extracting a crown feature point from the tree point cloud data
  • Step S4 structuring the main branch skeleton point
  • Step S5 structuring the crown feature points and calculating the radius of each branch; Step S6, reconstructing a complete tree model according to the skeleton points and radii of all the branches that have been structured.
  • the invention adopts the technology of computer graphics processing to reconstruct a complete tree model from the scanned three-dimensional tree point cloud data.
  • the invention automatically and accurately in a complex point cloud through comprehensive analysis of the local geometrical relationship of the point cloud and the spatial position relationship.
  • the skeleton point of the main branch is calculated and the radius is obtained, and the crown shape is completely restored.
  • a large number of fine branches are simulated in accordance with the biological characteristics, so that the reconstruction model obtains a higher realism on an accurate basis.
  • FIG. 1 is a flow chart of a method for reconstructing a point cloud and a data driven tree model according to the present invention
  • FIG. 2 is a view showing a point cloud data of a white pine tree obtained according to an embodiment of the present invention
  • FIG. Tree grading indicates a schematic diagram
  • FIG. 4 is a schematic view of a cylinder search space in accordance with an embodiment of the present invention.
  • FIG. 5 is a flow chart of a cylinder movement search method according to an embodiment of the present invention.
  • FIG. 6 is a main branch skeleton point extracted by a fractional ion current according to an embodiment of the present invention
  • FIG. 7 is a schematic diagram showing the result of branch and leaf separation according to an embodiment of the present invention
  • FIG. 8 is a schematic diagram showing the result of extracting the feature points of the canopy according to an embodiment of the present invention
  • FIG. 9 is a schematic diagram showing the structure of the main branches and the crowns according to an embodiment of the present invention
  • FIG. 10 is a reconstruction according to an embodiment of the present invention. a complete white pine tree model
  • Figure 11 is a comparison of the results of the present invention with the prior art in the case of input point cloud data occlusion
  • Figure 12 is a graphical representation of the results obtained by reconstructing a scanned forest point cloud using the present invention.
  • FIG. 1 is a flow chart of a point cloud and data driven tree model reconstruction method according to the present invention.
  • the method of the present invention includes the following steps: Sl, tree point cloud data acquisition and preprocessing, and defining a tree model. Graded representation; S2, main branch skeleton point extraction and branch and leaf separation; S3, canopy feature point extraction; S4, main branch skeleton point structure; S5, canopy feature point structure; S6, complete model reconstruction.
  • Step S1 acquiring tree point cloud data, pre-processing the same, and defining a hierarchical representation of the tree model;
  • the invention utilizes a three-dimensional laser scanner (eg.
  • Cyrax tools such as visual photography capture tree point cloud data, point cloud analysis techniques to preserve point clouds on single and multiple trees that need to be reconstructed, and to remove points from other objects, ie preprocessing.
  • 2 is a white pine tree scanning point cloud data acquired according to an embodiment of the invention.
  • the present invention classifies the shape of the tree: main branches, twigs and leaves, wherein the main branches contain 2 to 4 branches, and the twigs contain 2-5 branches, the specific series and Tree species and tree age are related.
  • the number of main branches is NL
  • the number of twigs is NS
  • twigs and leaves are combined to form a canopy.
  • 3 is a schematic diagram showing hierarchical representation of trees according to an embodiment of the present invention, taking 5 levels as an example, wherein FIG. 3A shows the main structure of trees, FIG. 3B shows elements of level 5, and FIG. 3C shows trees of level 5. model.
  • Step S2 extracting a main branch skeleton point and a radius thereof from the tree point cloud data, and performing branch and leaf separation processing;
  • the first step is to extract the main branch skeleton point from the point cloud data of the tree, and use the extracted main branch skeleton point to realize the separation of the branches and leaves, and then complete the structure of the main branch and the canopy in the subsequent steps.
  • the step S2 includes the following steps:
  • Step S2.1 calculating a local geometric quantity at each point p of the point cloud
  • the local geometric quantity at each point P of the point cloud includes a normal direction Q ⁇ , a principal curvature (» and k 2 (p) (fc 1 (p) ⁇ /c 2 (p)), and a main direction corresponding to the main curvature 3 ⁇ 4) and 0).
  • the step S2.1 further includes the following steps:
  • Step S2.1.1 establishing a kd tree of the entire point cloud data
  • the kd tree is one of the fastest data structures that have been proven to find neighbors. The method of building a kd tree is not described here.
  • Step S2.1.2 for each point p in the point cloud data, use the kd tree to find multiple of them, such as 15 or 30 neighbors, assuming that the neighbors are from the same plane, using the least squares method Combine this plane, using the normal vector of this plane as the normal direction n of point p (
  • the principal curvature / and fc 2 (p) at the point P and the main directions 3 ⁇ 4 and 3 ⁇ 40?) are calculated using the quadric surface. ;
  • Step S2.2 defining a cylinder fitting the p-shaped branch shape based on the local geometric quantity at each point p of the point cloud, and searching for a potential branch (ie, moving the cylinder method) by using the movement of the cylinder, thereby extracting Obtaining the main branch skeleton point and its radius;
  • the scanned data points are recorded as a set s).
  • FIG. 5 is a flow chart of a cylinder movement search method according to an embodiment of the present invention. As shown in FIG. 5, the cylinder movement search method includes the following steps:
  • Step S2.2.2 first determine whether any point q in the set Sd)) is valid, that is, if the cylinders S(p.) and S(q) satisfy: 1) b(q) 6 S(p.) 2) r(q)/r(p 0 ) - 1.0; 3) The angle between ⁇ and ⁇ ( ⁇ .) is small, where b(q) represents the center of curvature of the cylinder S(q), r (q) represents the radius of curvature of the cylinder S(q), r(p.) represents the radius of curvature of the cylinder S(p.), represents the axial direction of the cylinder S(q), and ⁇ ( ⁇ .) represents the cylinder In the axial direction of S(p.), the point q is referred to as a valid point; if the number of effective points in the set Sd)) is greater than the first threshold N x (set to 6 in an embodiment of the present invention), the initial The cylinder S (the successor cylinder S( 2 )
  • Step S2.2.4 the S (2) or S - continues until the search does not find any subsequent cylinder (2) on the basis of the number of cylinders if found N 2 than the second threshold value (in the embodiment of the present invention, a In the example set to 6), the cylinders form a main branch segment, and the center of the circle is recorded as the main branch skeleton point;
  • Figure 6 is a schematic diagram of the main branch skeleton points extracted in accordance with an embodiment of the present invention.
  • these skeleton points are not completely connected, and each skeleton point is represented by its corresponding cylinder (including the center and the radius of the mouth).
  • Step S2. Using the extracted main branch skeleton points, the point cloud data is divided into two parts: a point cloud on the main branch and a point cloud on the crown to realize separation of the branches and leaves;
  • the point cloud data is divided into a point cloud on the main branch and a point cloud on the crown.
  • the point cloud located in the column search area is considered to be a point cloud located on the main branch, and other point clouds are considered It is a point cloud located in the canopy.
  • the search radius of the cylindrical search area is ⁇ , where rp) is the radius of the cylinder, which is considered to be the radius of the main branch at the position, in an embodiment of the present invention, the input is ⁇ , so the search area of the cylinder is more realistic.
  • the range of branches is large, and while the point clouds on the main branches are all located in the search area, some of the point clouds on the crown are enveloped.
  • Figure 7 shows the results of the separation of the branches of the Pinus bungeana according to an embodiment of the present invention.
  • the point cloud (the portion of the gray scale) on the main branch is recorded as ⁇ ⁇
  • Step S3 extracting a crown feature point from the tree point cloud data
  • a multi-scale method is constructed to extract a crown feature point in the tree model.
  • each scale corresponds to a first-level representation of the tree, that is, the NS scales ⁇ 2 , ..., n NS corresponds to the NS-level twig representation of the tree, and ni
  • ⁇ For each scale ⁇ , divide the bounding box of the crown point cloud into several uniform and equal sizes (the size of the cube.
  • FIG. 8 is a schematic diagram showing the results of extracting feature points of a canopy according to an embodiment of the invention. In the figure, points with brighter gray scales (shown by small balls) are feature points, and these feature points are evenly distributed in space, and can be better. The ground shows the shape of the canopy.
  • Step S4 structuring the main branch skeleton point
  • the main branch skeleton points obtained by the foregoing steps do not have a complete connection relationship, that is, the skeleton points inside each of the obtained main branches have a connection relationship, but there is no connection information between the main branches and the main branches, and three of the crowns are
  • the feature points of the scale are also completely discrete. Therefore, in an embodiment of the present invention, the skeleton points are effectively and accurately structured by using a three-dimensional hierarchical particle flow motion method to construct a hierarchical branch.
  • each branch in ⁇ places the particle ⁇ with its lower end point as the initial position of the particle, and finally uses the remaining main branch skeleton point as the attraction to guide the particle to run in space, find a suitable connectable point, and use the particle
  • the trajectory is used as a supplement to the main branch to connect the scattered branches to achieve the structuring of all the main branch points.
  • each particle in the process of searching for connectable points, before tapping its own connectable points is constantly performing a cone-like hierarchical search.
  • the purpose of the cone-shaped hierarchical search is to find the branches that can be connected. As follows: Let the representative particle ⁇ / in the speed direction of 1 ,, ⁇ / record at the position of 1 ,, that is, the trajectory point of the particle running, for the particle located at ⁇ / position, ⁇ / as the apex, as the axis, Take ⁇ . High, to. Establish a cone for the angle between the busbar and the axis
  • Step S5 structuring the crown feature points and calculating the radius of each branch; after fully connecting the main branch skeleton points, in the step, the corresponding tree first-level canopy feature points are used as the starting point of the particle flow To the main branch skeleton point, the trajectory of the particle running is used as the skeleton point of the fine branch; the crown feature points corresponding to other levels of the tree are moved as the starting point of the particle flow to the upper primary skeleton point, and the trajectory of the particle still remains. As the skeleton point of the level, the structuring of all the feature points is realized; finally, the radius of each branch is estimated according to the radius of the main branch.
  • the structure of the canopy feature points has a great similarity with the structure of the main branch skeleton points.
  • set the canopy attraction point X for example, you can select one third of the tree height above the root node G
  • NS respectively.
  • the crown feature points of the scales are the particles at the beginning of the particle flow, and the NS scale particles are divided into NS batches to move toward the main branch skeleton point under the guidance of the main branch skeleton point and the attraction point X, so as to find a suitable one.
  • FIG. 9 is a schematic diagram showing the results of structuring of the branches of the main branches and the crowns according to an embodiment of the present invention.
  • the present invention sets the radius of the root of the twig to a fixed multiple of the radius of the parent branch. For example, 0.7 times, and the tip radius of the twig is set to be a fixed ⁇ . In an embodiment of the invention, ⁇ is set to 0.2 cm.
  • the radius of the intermediate skeleton point of the twig is obtained by linear fitting.
  • Step S6 reconstructing a complete tree model according to the skeleton points and radii of all the branches that have been structured.
  • the branches are fitted with cylinders of different radii (triangular mesh model), and the three-dimensional mesh model of the trees is reconstructed; then texture synthesis is adopted.
  • the method adds a suitable texture to the mesh model (the texture addition method belongs to the texture processing method commonly used in the prior art, which will not be described here), and then according to the size of the input point cloud data and the type of the leaf (broad or coniferous)
  • the statistical estimation method is used to determine the leaf information such as the number of leaves required, the length and width of the leaves, and different leaves are added to the end of the three-dimensional mesh model twig, thereby realizing the complete reconstruction of the tree three-dimensional model.
  • Figure 10 is a complete three-dimensional tree model reconstructed from scanned white-skin tree point cloud data using the method of the present invention, which includes the main branches, canopies, and leaves.
  • the reconstructed tree model is very consistent with the shape of the input point cloud, which is reflected in two aspects: 1) The point cloud on the main branch is in good agreement with the reconstructed model, which illustrates this
  • the main branch reconstruction of the invention is accurate in both the position and the radius of the skeleton point; 2)
  • the crown portion completely fills the entire canopy space, and accurately reduces the concave and convex features of the crown.
  • the method of Livny 2010 shown in FIG.
  • the method of the invention can also be easily applied to model reconstruction aspects of trees.
  • the difference between the model reconstruction method of the forest point cloud data and the reconstruction method of the single tree is that the root node G and the canopy attraction point X are first set, and the rest are similar.
  • the present invention establishes two sectional planes parallel to the ground plane from the ground plane at a height of 1/4 and 1/3 of the ground plane in space, and the skeleton point between the section planes is called an alternative point attraction point.
  • each point automatically searches for the closest point to its own Euclidean distance from the candidate attraction points during the motion as its own attraction point, so as to attract the point at the ground plane.
  • the upper projection point is its own root node.
  • the feature point is structured by the particle flow motion.
  • Fig. 12A is input forest point cloud data
  • Fig. 12B is a forest model obtained by reconstruction using the method of the present invention.
  • the method of the invention automatically and accurately calculates the skeleton points of the branches in the complex tree point cloud model and obtains the accurate radius by comprehensively analyzing the local geometrical relationship of the point cloud and the spatial position relationship, and changes the manual interaction in the previous trunk extraction algorithm. More, the workload is large, and the results are not accurate enough.
  • the method of grading particle flow motion automatically realizes the connection of the main branch skeleton points and the comprehensive characterization of the canopy feature points and the skeleton skeleton points, which completely restores the crown shape. And it is in line with the biological characteristics to simulate a large number of twigs, so that the reconstruction model obtains a higher sense of reality on an accurate basis.

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Abstract

L'invention concerne un procédé pour reconstruire un modèle d'arbre sur la base d'un nuage de points et d'un entraînement de données. Le procédé comprend les étapes suivantes : des données de nuage de points d'arbre sont acquises et prétraitées, et une représentation de classification du modèle d'arbre est définie ; un procédé de déplacement de cylindre est prévu et utilisé pour extraire des points de structure de branche principale à partir des données de nuage de points d'arbre, et un processus de séparation de branche et de feuille est réalisé ; des points de caractéristique de couronne sont extraits à partir des données de nuage de points d'arbre ; un procédé de flux d'ions de classification est prévu et utilisé pour structurer les points de structure de branche principale et les points de caractéristique de couronne ; le modèle d'arbre complet est obtenu au moyen d'une reconstruction sur la base des points de structure structurés et des rayons de toutes les branches. Une solution est fournie pour reconstruire le modèle d'arbre complet dans les données de nuage de points tridimensionnels, le modèle reconstruit obtenu et un nuage de points d'origine ont un degré élevé d'ajustement, et le bon résultat de reconstruction peut être obtenu sur des modèles qui sont sérieusement bloqués et ont une forme complexe.
PCT/CN2014/074634 2014-04-02 2014-04-02 Procédé pour reconstruire un modèle d'arbre sur la base d'un nuage de points et d'un entraînement de données Ceased WO2015149302A1 (fr)

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