CN103942838A - Point cloud data based single tree three-dimensional modeling and morphological parameter extracting method - Google Patents
Point cloud data based single tree three-dimensional modeling and morphological parameter extracting method Download PDFInfo
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Abstract
The invention relates to a point cloud data based single tree three-dimensional modeling and morphological parameter extracting method. The point cloud data based single tree three-dimensional modeling and morphological parameter extracting method comprises obtaining three-dimensional surface point cloud data of high density standing trees through a three-dimensional scanner or other live-action measuring modes, calculating the shortest distance from points to root nodes through a k-nearest neighbor graph, performing hierarchical clustering on the data according to distance, enabling centers of clustering hierarchies to be served as framework points of a limb system and meanwhile extracting corresponding semi-diameter of the framework points; connecting the framework points to establish a topological structure of branches and grading the branches; performing three-dimensional geometrical reconstruction on branches through a generalized cylinder body; adding leaf models to the limb system to form into a vivid three-dimensional single tree model; extracting height of trees, diameter of breast height and crown breadth of the standing trees in the point cloud. The point cloud data based single tree three-dimensional modeling and morphological parameter extracting method can rapidly and semi-automatically extract tree important geometrical parameters and topological information to form into the high vivid single tree geometric model and has wide application prospects and values in fields such as agriculture and forestry survey, ecological research and landscape planning.
Description
Technical field
The present invention relates to belong to IT application to agriculture field, relate to and utilize spatial digitizer and the three-dimensional surface structured data (some cloud) photogrammetric, computer vision technique obtains trees, and according to cloud data, the three-dimensional configuration structure of trees is carried out the method for Treatment Analysis and reconstruction, particularly a kind of list based on cloud data is set the method for three-dimensional modeling and morphological parameters extraction.
Background technology
Phytomorph structured data is the basis of realizing plant three-dimensional geometry Morphology Remodeling and growth course simulation accurately and reliably.Only have and obtain geometric shape structure accurately, could in virtual environment, generate not only realistic physical dimension and morphological feature, but also have the plant individual of certain sense of reality.As botanic important component part, because trees morphosis is complicated and changeable, it is carried out to sense of reality three-dimensional geometry and rebuild the object that becomes the common concern in virtual forest (plant) environment, virtual reality research field.How fast, obtain accurately and efficiently the three-dimensional spatial information (containing geometric scale parameter, how much topological relations) of individual trees, become the primary problem solving of trees high fidelity three-dimensional modeling.Existing plant object geological information acquisition methods is the means based on hand dipping data and Digital Design mostly, and traditional data acquisition technology speed is slow, precision is low, is difficult to meet the requirement of modern plants Geometric Modeling and application in accuracy and the sense of reality.
The develop rapidly of 3-D scanning and photogrammetric technology in recent years, for the parameter extraction of trees morphosis and reconstruction true to nature provide brand-new, an active data acquisition methods.3-D scanning technology can directly be obtained the three dimensions cloud data of high precision, highdensity body surface, has untouchable, the feature such as sweep velocity is fast, real-time, precision is high, initiative is strong, digital feature.
Utilize cloud data to carry out tree parameters extraction and three-dimensional modeling, early stage work utilizes a series of cylinder models of B-spline surface matching to express limb, this method be only applicable to without leaf, the typical trees of limb feature.In recent years, new method continued to bring out, and can roughly be divided into substep modeling method and directly utilize cloud data automatically to build the large class of Three-dimension Tree model two.First substep modeling method obtains the call parameter of trees modeling from a cloud, then these parameters is input in existing trees modeling software and builds three-dimensional model; Automated process completes all processes of modeling an internal system.
Due to the interference in masking phenomenon and environment, the standing tree point cloud alive of acquisition has shortage of data, skewness conventionally, contain the features such as noise need to overcome; According to this cloud data, rebuild the trees three-dimensional model obtaining and inevitably have some disappearances and error; Existing method and system fails to provide interactively means to make user can revise the model of change local error.
The patent No. is ZL201010188292.8, in the patent of denomination of invention for " tree point cloud data based in the method for reconstructing three-dimensional model of cutting apart with automatic growth ", a kind of scan-data that only utilizes laser scanner has been proposed, obtain the method for the Three-dimension Reconstruction Model of faithful to original material object, the method obtains the reconstruction model of tree point cloud data by the growth of the withe of Data Segmentation and branch.But the weak point of this method for reconstructing, mainly be a cloud to be divided into different organs according to principal curvatures, because the space distribution of cloud data is uneven, the method is effective in the intensive trunk portion of data point, but other cover seriously, data point sparse region, its segmentation effect may be not good, and reconstructed results is affected.
Application number is CN201310014769.4, in the patent that denomination of invention is " a kind of method and system with leaf state trees morphosis three-dimensional reconstruction ", propose a kind of method and system with leaf state trees morphosis three-dimensional reconstruction, realized the quick three-dimensional reconstructing with leaf state tree morphosis.But the weak point of this method for reconstructing, is mainly according to different color segmentations, to be different organs by a cloud, more respectively each organ is rebuild.Cut apart based on color and carry out, and color data not every kind of digitized instrument can produce, in cloud data, may lack RGB property value.
The advantage of the inventive method is to process the cloud data (as terrestrial Laser scanner, spatial digitizer and photogrammetric) of separate sources; Can directly utilize former data to carry out classification to tree branches, set up limb system, then utilize the mode of interactive editing, increase withe and leaf, make up factor data disappearance and the incomplete problem of model.
Summary of the invention
The object of the present invention is to provide a kind of cloud data that can process separate sources; Can directly utilize former data to carry out classification to tree branches, set up limb system, then utilize the mode of interactive editing, increase withe and leaf, make up factor data disappearance and the single tree three-dimensional modeling based on cloud data of the incomplete problem of model and the method for morphological parameters extraction.
For achieving the above object, technical scheme of the present invention is: a kind of method that single tree three-dimensional modeling and morphological parameters based on cloud data extracted, comprise the steps,
Steps A: adopt terrestrial Laser scanner to obtain cloud data the pre-service of single standing tree alive, build the store and management that Kd-tree data tree structure carries out data;
Step B: determine the root node of trunk in some cloud, set up some cloud k nearest neighbor figure, calculate each point to the bee-line of root node,, calculate the center of each cluster level, thereby obtain the skeleton point of limb data hierarchy cluster by distance;
Step C: set up the topological structure of limb skeleton point, limb is carried out hierarchically organized, estimation limb, at the radius of skeleton point position, is used generalized cylinder to carry out three-dimensional geometry to limb and rebuild formation limb system;
Step D: set up leaf three-dimensional model, adopt interactive mode to add leaf model in limb system, form three-dimensional model true to nature;
Step e: the trees morphosis parameter of directly extracting the height of tree, the diameter of a cross-section of a tree trunk 1.3 meters above the ground and hat width from cloud data.
In embodiments of the present invention, described step B, specifically comprises the following steps:
Step B1: utilize the Kd-Tree structure of cloud data, each data point is carried out to k nearest neighbor search, or carry out range searching with radius R, connect search point and nearest neighbor point, make to form connected relation between them, travel through successively all points, complete the establishment of Neighborhood Graph;
Step B2: interactive select or by the root node as trees to the definite center of circle of the round matching of base point cloud, represent a point of the extreme lower position of trees trunk;
Step B3: according to the Neighborhood Graph building, calculate each point to the shortest path of root node, the length of shortest path is this point to the geodesic distance of root node, and the single source path figure forming thus, is called geodetic figure;
Step B4: according to the difference of geodesic distance, by cloud data hierarchical cluster, the number of layering is determined by the complicacy of trees and the resolution requirements of model;
Step B5: the center of calculation level cloud cluster at all levels or the position of center of gravity, as the skeleton point of limbs at different levels.
In embodiments of the present invention, the K value of described step B1 or the size of R value are determined according to scanning accuracy and the quality of data.
In embodiments of the present invention, described step B3, has adopted a kind of improved dijkstra's algorithm, specifically comprises the following steps:
Step B31: definition and initialization root node P, in abutting connection with array isjoin, definition forerunner array predrive and path distance array dis, wherein in abutting connection with array, be used for storing distance matrix, forerunner's array is for depositing the previous point of each point of shortest path, and path distance array is for depositing other point to the distance of root node;
Step B32: according in Neighborhood Graph in abutting connection with array initialization path apart from array, there is no the infinity that is initialized as of distance value;
Step B33: calculating path, apart from minimum value in array, is designated as w;
Step B34: the Neighbor Points j of traversal w, relatively j point adds the magnitude relationship of the distance sum between w, j point to distance and the w point of root node to the distance of root node, using less value as j, point is to the distance of root node, and w is put into forerunner's array of j, otherwise judge next Neighbor Points, until whole circulation finishes, the value in forerunner's array is each point to the shortest path of root node.
In embodiments of the present invention, described step C, specifically comprises the following steps:
Step C1: connecting framework point is set up tree construction, and skeleton is put to corresponding branch and carry out topological classification, trunk is defined as the 0th grade, and the branch growing out from trunk belongs to the first order, and the branch growing out from first order branch is the second level, by that analogy;
Step C2: utilize the traversal number of times of skeleton point, estimation skeleton is put the radius of corresponding branch;
Step C3: according to each skeleton point and corresponding radius and topological relation, adopt generalized cylinder n-body simulation n branch, set up the 3-D geometric model of limb system.
In embodiments of the present invention, in described step C1, skeleton is put the topological classification of corresponding branch, is specifically comprised the following steps:
Step C11: from root node, skeleton point in first order point cloud is as child's node of root node, the like, next stage skeleton point is as child's node of upper level skeleton point, if while there is a plurality of skeleton point in same level, the skeleton point in its next level, according to the method for spatial neighbors, search in last layer level from own nearest skeleton point as father node, thereby build the tree construction of whole skeleton;
Step C12: give direction value of each skeleton point and traversal time numerical value, the direction of skeleton point is pointed to the direction vector of this skeleton point for its father node, and traversal number of times is other number of times of putting the current point of path process that arrives root node;
Step C13: from root node, search for successively its child's node, when the angle theta of the direction of child's node and the direction of its father node is less than certain threshold value δ, and the traversal number of child's node is greater than certain threshold epsilon, using this child nodes as trunk node, and add up child's number of network nodes; If nodal point number only has 1, next node in trunk path using it, nodal point number, more than 1, selects the minimum point of angle theta as next node in trunk path, simultaneously the initial growth point using current node as next rank branch;
Step C14: initial growth point from step C13 starts, according to the process identification primary branches described in step C13, the rest may be inferred, when having traveled through all skeleton points, can complete branch classification after identifying the path of all branches.
In embodiments of the present invention, in described step C2, the estimation process of branch radius is: by skeleton traversal of tree, obtain child's number of each skeleton node subordinate, be the traversal number of times VisitNum of this skeleton point, this skeleton is put corresponding radius
, wherein r is related coefficient, relevant with a cloud density.
In embodiments of the present invention, in described step C3, utilize generalized cylinder to build limb geometric model, specifically comprise the following steps:
Step C31: take each skeleton point is true origin, the direction of skeleton point of take is Z axis, sets up local coordinate system;
Step C32: generate cross section circle according to the radius of skeleton point under local coordinate system;
Step C33: the point on adjacent cross section circle builds V-belt, forms the surface mesh model of limb.
In embodiments of the present invention, described step D sets up vivid three dimensional list tree-model and specifically comprises the following steps:
Step D1: set up leaf three-dimensional model;
Step D2: on the basis of three-dimensional limb system geometric model, fit cloud data is as constraint and reference, and interactively adds leaf model on the branch of appropriate level;
Step D3: by texture, lighting simulation, three-dimensional model is played up, formed single tree three-dimensional model of the sense of reality.
In embodiments of the present invention, in described step e, the extraction of trees morphosis parameter specifically comprises the following steps:
Step e 1: calculate the root points of trees and the difference in height of treetop point in cloud data by asking, obtain the height of tree;
Step e 2: the circle or the ellipse that converge apart from the point in lower 5 cm range on 1.3 meters of root points by least square fitting, the diameter of a cross-section of a tree trunk 1.3 meters above the ground that the mean value of circular diameter or ellipse long and short shaft is trees;
Step e 3: cloud data vertical projection, on a surface level, is calculated to the convex closure body of its subpoint, connect any two points on convex closure body and calculate the distance between 2, then obtain maximum range value Pmax, as the maximum hat width of trees.
Compared to prior art, the present invention has following beneficial effect:
The invention has the beneficial effects as follows a kind of single method of setting three-dimensional reconstruction and morphological feature extraction based on cloud data that proposed, directly from the three-dimensional surface cloud data of high precision trees, extract limb system, in conjunction with interactive mode, articulate the organs such as leaf, can obtain quickly and efficiently the morphosis parameters of trees and strong sense of reality, accurate three-dimensional model, thereby for agriculture and forestry investigation, radiomimesis, leaf area index estimation, vegetation quantitative remote sensing provide high precision model, for digital entertainment, Garden Planning etc. provides model true to nature.
Accompanying drawing explanation
Fig. 1 is the concrete techniqueflow schematic diagram of implementing of the inventive method.
Fig. 2 is that trees skeleton point extracts process flow diagram.
Fig. 3 is geodetic figure.
Fig. 4 is improved dijkstra's algorithm process flow diagram.
Fig. 5 is the cloud data slice map of Schima superba.
Fig. 6 is trees skeleton point and partial enlarged drawing.
Fig. 7 is skeleton automatic classification process flow diagram.
Fig. 8 is skeleton hierarchy.
Fig. 9 a is the three-dimensional model of Schima superba cloud data.
Fig. 9 b is the three-dimensional model of Schima superba limb.
Figure 10 is that tree modelling is rebuild design sketch.
Figure 11 is that trees hat width extracts result.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is specifically described.
The method that a kind of single tree three-dimensional modeling and morphological parameters based on cloud data of the present invention extracted, comprises the steps,
Steps A: adopt terrestrial Laser scanner to obtain cloud data the pre-service of single standing tree alive, build the store and management that Kd-tree data tree structure carries out data;
Step B: determine the root node of trunk in some cloud, set up some cloud k nearest neighbor figure, calculate each point to the bee-line of root node,, calculate the center of each cluster level, thereby obtain the skeleton point of limb data hierarchy cluster by distance;
Step C: set up the topological structure of limb skeleton point, limb is carried out hierarchically organized, estimation limb, at the radius of skeleton point position, is used generalized cylinder to carry out three-dimensional geometry to limb and rebuild formation limb system;
Step D: set up leaf three-dimensional model, adopt interactive mode to add leaf model in limb system, form three-dimensional model true to nature;
Step e: the trees morphosis parameter of directly extracting the height of tree, the diameter of a cross-section of a tree trunk 1.3 meters above the ground and hat width from cloud data.
The following embodiment of the present invention is example in conjunction with Schima superba and mango, concrete expansion explanation.
The invention provides the single method of setting three-dimensional reconstruction and morphological feature extraction based on cloud data, as shown in Figure 1, specifically comprise the following steps:
Steps A: adopt terrestrial Laser scanner or alternate manner to obtain cloud data the pre-service of single standing tree alive, build the store and management that Kd-tree data tree structure carries out data.
Step B: determine the root node of trunk in some cloud, set up some cloud k nearest neighbor figure, calculate each point to the bee-line of root node, press distance by data hierarchy cluster, calculate the center of each cluster level, thereby obtain the skeleton point of limb, its algorithm realization flow as shown in Figure 2; Specifically comprise the following steps:
Step B1: the Kd-Tree structure of utilizing cloud data, each data point is carried out to k nearest neighbor search, or carry out range searching with radius R, connect search point and nearest neighbor point, make to form between them connected relation, travel through successively all points, complete the establishment of Neighborhood Graph, in the present embodiment, the value size of K and R is determined according to scanning accuracy and the quality of data; Conventionally high to dot density, more complete data, K can select 5; And for the optional 10-15 of the serious data K of sparse disappearance;
Step B2: interactive select or by the root node as trees to the definite center of circle of the round matching of base point cloud, represent a point of the extreme lower position of trees trunk;
Step B3: according to the Neighborhood Graph building, calculate each point to the shortest path of root node, the length of shortest path is this point to the geodesic distance of root node, and the single source path figure forming thus, is called geodetic figure, as shown in Figure 3; Use improved dijkstra's algorithm (Fig. 4) to calculate geodesic distance, specifically comprise the following steps:
Step B31: definition and initialization root node P, in abutting connection with array isjoin, definition forerunner array predrive and path distance array dis, wherein in abutting connection with array, be used for storing distance matrix, forerunner's array is for depositing the previous point of each point of shortest path, and path distance array is for depositing other point to the distance of root node;
Step B32: according in Neighborhood Graph in abutting connection with array initialization path apart from array, there is no the infinity that is initialized as of distance value;
Step B33: calculating path, apart from minimum value in array, is designated as w;
Step B34: the Neighbor Points j of traversal w, relatively j point adds the magnitude relationship of the distance sum between w, j point to distance and the w point of root node to the distance of root node, using less value as j, point is to the distance of root node, and w is put into forerunner's array of j, otherwise judge next Neighbor Points, until whole circulation finishes, the value in forerunner's array is each point to the shortest path of root node;
Step B4: according to the difference of geodesic distance, by cloud data hierarchical cluster, the number of layering is determined by the complicacy of trees and the resolution requirements of model, and the present embodiment be take Schima superba as example, and layering number is 54 layers, and layering the results are shown in Figure 5;
Step B5: the center of calculation level cloud cluster at all levels or the position of center of gravity, as the skeleton point of limbs at different levels, the skeleton point that the present embodiment extracts is shown in Fig. 6.
Step C: set up the topological structure of limb skeleton point, limb is carried out to hierarchically organized (trunk, primary branches, secondary branch etc.), estimation limb, at the radius of skeleton point position, is used generalized cylinder to carry out three-dimensional geometry reconstruction to limb.Specific implementation comprises the following steps:
Step C1: connecting framework point is set up tree construction, and skeleton is put to corresponding branch and carry out topological classification, trunk is defined as the 0th grade, and the branch growing out from trunk belongs to the first order, and the branch growing out from first order branch is the second level, by that analogy; Branch hierarchical algorithms flow process is shown in Fig. 7, specifically comprises the following steps:
Step C11: from root node, skeleton point in first order point cloud is as child's node of root node, the like, next stage skeleton point is as child's node of upper level skeleton point, if while there is a plurality of skeleton point in same level, the skeleton point in its next level, according to the method for spatial neighbors, search in last layer level from own nearest skeleton point as father node, thereby build the tree construction of whole skeleton;
Step C12: give direction value of each skeleton point and traversal time numerical value, the direction of skeleton point is pointed to the direction vector of this skeleton point for its father node, and traversal number of times is other number of times of putting the current point of path process that arrives root node;
Step C13: from root node, search for successively its child's node, when the angle theta of the direction of child's node and the direction of its father node is less than certain threshold value δ, and the traversal number of child's node is greater than certain threshold epsilon, using this child nodes as trunk node, and add up child's number of network nodes; If nodal point number only has 1, next node in trunk path using it, nodal point number, more than 1, selects the minimum point of angle theta as next node in trunk path, simultaneously the initial growth point using current node as next rank branch;
Step C14: the initial growth point from step C13 starts, according to the process identification primary branches described in step C13, the rest may be inferred, when having traveled through all skeleton points, after identifying the path of all branches, can complete branch classification, the branch skeleton hierarchy of the present embodiment is shown in Fig. 8.
Step C2: utilize the traversal number of times of skeleton point, estimation skeleton is put the radius of corresponding branch.The estimation detailed process of branch radius is: by skeleton traversal of tree, obtain child's number of each skeleton node subordinate, be the traversal number of times VisitNum of this skeleton point, this skeleton is put corresponding radius
, wherein r is related coefficient, relevant with a cloud density, and the present embodiment r value is 0.03, and its value can contrast by the radius with simulating from True Data acquisition.This computing method conform to the pipeline theory of plant.
Step C3: according to each skeleton point and corresponding radius and topological relation, adopt generalized cylinder n-body simulation n branch, set up the 3-D geometric model of limb system.Concrete steps are as follows:
Step C31: take each skeleton point is true origin, the direction of skeleton point of take is Z axis, sets up local coordinate system;
Step C32: generate cross section circle according to the radius of skeleton point under local coordinate system;
Step C33: the point on adjacent cross section circle builds V-belt, forms the surface mesh model of limb, and the limb system of the formation of the present embodiment is shown in Fig. 9 a-9b.
Step D: set up leaf three-dimensional model, adopt interactive mode to add leaf model in limb system, form three-dimensional model true to nature.Specifically comprise the following steps:
Step D1: set up leaf three-dimensional model;
Step D2: on the basis of three-dimensional limb system geometric model, fit cloud data is as constraint and reference, and interactively adds leaf model on the branch of appropriate level;
Step D3: by texture, lighting simulation, three-dimensional model is played up, formed single tree three-dimensional model of the sense of reality, Figure 10 is the single tree three-dimensional model from the Schima superba of tree point cloud generation and mango.
Step e: the trees morphosis parameter of directly extracting the height of tree, the diameter of a cross-section of a tree trunk 1.3 meters above the ground and hat width from cloud data.Specifically comprise the following steps:
Step e 1: calculate the root points of trees and the difference in height of treetop point in cloud data by asking, obtain the height of tree;
Step e 2: the circle or the ellipse that converge apart from the point in lower 5 cm range on 1.3 meters of root points by least square fitting, the diameter of a cross-section of a tree trunk 1.3 meters above the ground that the mean value of circular diameter or ellipse long and short shaft is trees;
Step e 3: by cloud data vertical projection on a surface level, calculate the convex closure body of its subpoint, connect any two points on convex closure body and calculate the distance between 2, then obtain maximum range value Pmax, as the maximum hat width of trees, the convex closure that Figure 11 the has expressed one tree hat width width of coming of age.
By said method, the three-dimensional precision modeling of single tree and the trees morphological parameters that can fast and effeciently realize based on cloud data are accurately extracted.
On the Visual Studio of Microsoft 2008 platforms, utilize OpenGL graphics standard, realized the single tree three-dimensional modeling method based on cloud data, the trees modeling engine based in parametrization list tree modeling ParaTree of Bing Yuben research team exploitation is integrated; System has the functional modules such as Point Cloud Processing, single tree three-dimensional modeling, Visual Interactive editor, tree parameters extraction, mode input output.
(1) Point Cloud Processing module, the 3-D display such as, rotation painted for cloud data is carried out, convergent-divergent, and can interactively selection cloud data, hides, the editing and processing such as deletion;
(2) single tree three-dimensional modeling module, by visible user interface, extracts the skeleton point of the main branch of trees for user to cloud data, then skeleton point is carried out to automated topology classification, thereby generates the three-dimensional model of limb system.User can also adopt interactive mode to articulate the organs such as leaf and flower, fruit, by texture, illumination, is played up etc. and to be formed individual plants three-dimensional model true to nature;
(3) Visual Interactive editor module, for user provides visual interactive editor comparatively flexibly, adjusts the organ parameters such as leaf and flower, fruit and model information statistics etc.;
(4) tree parameters extraction module, obtains the morphological parameters information of the height of tree, the diameter of a cross-section of a tree trunk 1.3 meters above the ground and the hat width of trees for user;
(5) mode input output module, supports pctm form (system user-defined format) plant model file and mesh form organ mode input, supports form plant organ texture (as bark, the leaf flowers and fruits) inputs such as dds, tga, jpg, bmp.System can be output as the individual plants three-dimensional model of generation pctm form, COLLADA form and VRML three-dimensional model form.Wherein, the VRML form of output and COLLADA form list tree-model can be for third party softwares.
In the present embodiment, adopt Riegl VZ-400 laser scanner to obtain Schima superba and the mango in a century-old camphor tree and campus, the cloud data of 3 trees is by pretreated some cloud number as table 1, and the Model Reconstruction time is as shown in table 1.
Be more than preferred embodiment of the present invention, all changes of doing according to technical solution of the present invention, when the function producing does not exceed the scope of technical solution of the present invention, all belong to protection scope of the present invention.
Claims (10)
1. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data extracted, is characterized in that: comprises the steps,
Steps A: adopt terrestrial Laser scanner to obtain cloud data the pre-service of single standing tree alive, build the store and management that Kd-tree data tree structure carries out data;
Step B: determine the root node of trunk in some cloud, set up some cloud k nearest neighbor figure, calculate each point to the bee-line of root node,, calculate the center of each cluster level, thereby obtain the skeleton point of limb data hierarchy cluster by distance;
Step C: set up the topological structure of limb skeleton point, limb is carried out hierarchically organized, estimation limb, at the radius of skeleton point position, is used generalized cylinder to carry out three-dimensional geometry to limb and rebuild formation limb system;
Step D: set up leaf three-dimensional model, adopt interactive mode to add leaf model in limb system, form three-dimensional model true to nature;
Step e: the trees morphosis parameter of directly extracting the height of tree, the diameter of a cross-section of a tree trunk 1.3 meters above the ground and hat width from cloud data.
2. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data according to claim 1 extracted, is characterized in that: described step B, specifically comprises the following steps:
Step B1: utilize the Kd-Tree structure of cloud data, each data point is carried out to k nearest neighbor search, or carry out range searching with radius R, connect search point and nearest neighbor point, make to form connected relation between them, travel through successively all points, complete the establishment of Neighborhood Graph;
Step B2: interactive select or by the root node as trees to the definite center of circle of the round matching of base point cloud, represent a point of the extreme lower position of trees trunk;
Step B3: according to the Neighborhood Graph building, calculate each point to the shortest path of root node, the length of shortest path is this point to the geodesic distance of root node, and the single source path figure forming thus, is called geodetic figure;
Step B4: according to the difference of geodesic distance, by cloud data hierarchical cluster, the number of layering is determined by the complicacy of trees and the resolution requirements of model;
Step B5: the center of calculation level cloud cluster at all levels or the position of center of gravity, as the skeleton point of limbs at different levels.
3. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data according to claim 2 extracted, is characterized in that: the K value of described step B1 or the size of R value are determined according to scanning accuracy and the quality of data.
4. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data according to claim 2 extracted, is characterized in that: described step B3, adopted a kind of improved dijkstra's algorithm, and specifically comprise the following steps:
Step B31: definition and initialization root node P, in abutting connection with array isjoin, definition forerunner array predrive and path distance array dis, wherein in abutting connection with array, be used for storing distance matrix, forerunner's array is for depositing the previous point of each point of shortest path, and path distance array is for depositing other point to the distance of root node;
Step B32: according in Neighborhood Graph in abutting connection with array initialization path apart from array, there is no the infinity that is initialized as of distance value;
Step B33: calculating path, apart from minimum value in array, is designated as w;
Step B34: the Neighbor Points j of traversal w, relatively j point adds the magnitude relationship of the distance sum between w, j point to distance and the w point of root node to the distance of root node, using less value as j, point is to the distance of root node, and w is put into forerunner's array of j, otherwise judge next Neighbor Points, until whole circulation finishes, the value in forerunner's array is each point to the shortest path of root node.
5. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data according to claim 1 extracted, is characterized in that: described step C, specifically comprises the following steps:
Step C1: connecting framework point is set up tree construction, and skeleton is put to corresponding branch and carry out topological classification, trunk is defined as the 0th grade, and the branch growing out from trunk belongs to the first order, and the branch growing out from first order branch is the second level, by that analogy;
Step C2: utilize the traversal number of times of skeleton point, estimation skeleton is put the radius of corresponding branch;
Step C3: according to each skeleton point and corresponding radius and topological relation, adopt generalized cylinder n-body simulation n branch, set up the 3-D geometric model of limb system.
6. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data according to claim 5 extracted, is characterized in that: in described step C1, skeleton is put the topological classification of corresponding branch, specifically comprised the following steps:
Step C11: from root node, skeleton point in first order point cloud is as child's node of root node, the like, next stage skeleton point is as child's node of upper level skeleton point, if while there is a plurality of skeleton point in same level, the skeleton point in its next level, according to the method for spatial neighbors, search in last layer level from own nearest skeleton point as father node, thereby build the tree construction of whole skeleton;
Step C12: give direction value of each skeleton point and traversal time numerical value, the direction of skeleton point is pointed to the direction vector of this skeleton point for its father node, and traversal number of times is other number of times of putting the current point of path process that arrives root node;
Step C13: from root node, search for successively its child's node, when the angle theta of the direction of child's node and the direction of its father node is less than certain threshold value δ, and the traversal number of child's node is greater than certain threshold epsilon, using this child nodes as trunk node, and add up child's number of network nodes; If nodal point number only has 1, next node in trunk path using it, nodal point number, more than 1, selects the minimum point of angle theta as next node in trunk path, simultaneously the initial growth point using current node as next rank branch;
Step C14: initial growth point from step C13 starts, according to the process identification primary branches described in step C13, the rest may be inferred, when having traveled through all skeleton points, can complete branch classification after identifying the path of all branches.
7. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data according to claim 5 extracted, it is characterized in that: in described step C2, the estimation process of branch radius is: by skeleton traversal of tree, obtain child's number of each skeleton node subordinate, the traversal number of times VisitNum that is this skeleton point, this skeleton is put corresponding radius
, wherein r is related coefficient, relevant with a cloud density.
8. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data according to claim 5 extracted, is characterized in that: in described step C3, utilize generalized cylinder to build limb geometric model, specifically comprise the following steps:
Step C31: take each skeleton point is true origin, the direction of skeleton point of take is Z axis, sets up local coordinate system;
Step C32: generate cross section circle according to the radius of skeleton point under local coordinate system;
Step C33: the point on adjacent cross section circle builds V-belt, forms the surface mesh model of limb.
9. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data according to claim 1 extracted, is characterized in that: described step D sets up vivid three dimensional list tree-model and specifically comprises the following steps:
Step D1: set up leaf three-dimensional model;
Step D2: on the basis of three-dimensional limb system geometric model, fit cloud data is as constraint and reference, and interactively adds leaf model on the branch of appropriate level;
Step D3: by texture, lighting simulation, three-dimensional model is played up, formed single tree three-dimensional model of the sense of reality.
10. the method that single tree three-dimensional modeling and the morphological parameters based on cloud data according to claim 1 extracted, is characterized in that: in described step e, the extraction of trees morphosis parameter specifically comprises the following steps:
Step e 1: calculate the root points of trees and the difference in height of treetop point in cloud data by asking, obtain the height of tree;
Step e 2: the circle or the ellipse that converge apart from the point in lower 5 cm range on 1.3 meters of root points by least square fitting, the diameter of a cross-section of a tree trunk 1.3 meters above the ground that the mean value of circular diameter or ellipse long and short shaft is trees;
Step e 3: cloud data vertical projection, on a surface level, is calculated to the convex closure body of its subpoint, connect any two points on convex closure body and calculate the distance between 2, then obtain maximum range value Pmax, as the maximum hat width of trees.
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