Analysis method and system for tree crown layer based on point cloud data

Abstract

The invention provides a tree crown layer analysis method and system based on point cloud data, and relates to the point cloud data processing technology, and the method comprises the steps: obtaining an original point cloud data set of forestry resources; preprocessing the original point cloud data set to obtain a preprocessed point cloud data set; on the basis of the preprocessed point cloud data set, an octree index division space is constructed, single trees are separated by adopting a connectivity clustering algorithm, and a point cloud segmentation result carrying labels is generated; on the basis of segmentation data of each plant in the point cloud segmentation result, tree parameters are extracted, and a structured parameter table is generated; generating a 3D cutting frame in real time according to the input of the user, and screening and outputting a cutting point cloud subset of the target area based on the 3D cutting frame and the single tree; and performing equal-thickness layered slicing along the vertical direction based on the cut point cloud subset, calculating the projection area layer by layer and accumulating the volume of each layer, and generating the total volume of the crown. Based on the scheme, the tree parameters of the crown layer can be accurately analyzed.

Description

Technical Field

[0001] The present invention relates to point cloud data processing technology, and in particular to a method and system for analyzing tree crown layers based on point cloud data. Background Art

[0002] Currently in forestry surveys, handheld lidar is widely used to measure parameters such as forestry structure, crown coverage, tree height, diameter, etc.

[0003] Typically, the point cloud data obtained by a handheld laser radar (LiDAR) usually contains a large amount of background information (such as the ground, bushes, and irrelevant trees). Directly processing these raw point clouds will result in high computational overhead and difficulty in target recognition. Therefore, the point cloud data needs to be cropped.

[0004] Traditional point cloud clipping technology is usually based on regular shapes for clipping, such as using rectangular or spherical clipping boxes, which lack the ability to adapt to terrain and tree morphology. Therefore, it is impossible to accurately clip point cloud data and thus cannot accurately determine forestry parameters. Summary of the invention

[0005] The purpose of the present invention is to address the problem that forestry parameters cannot be accurately determined in the prior art, and to provide a tree canopy analysis method and system based on point cloud data, which can accurately determine forestry parameters based on forestry point cloud data.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for analyzing tree crown layers based on point cloud data, the method comprising: obtaining an original point cloud data set of forestry resources; preprocessing the original point cloud data set to obtain a preprocessed point cloud data set; constructing an octree index partition space based on the preprocessed point cloud data set, and using a connectivity clustering algorithm to separate individual trees to generate a point cloud segmentation result with labels; extracting tree parameters based on the segmentation data of each tree in the point cloud segmentation result to generate a structured parameter table; the tree parameters include at least one of the diameter at breast height, height, and trunk diameter of each tree; in the case of a single tree analysis requirement, generating a three-dimensional (3D) cropping frame in real time according to user input, and filtering and outputting a cropped point cloud subset of the target area based on the 3D cropping frame and the single tree; based on the cropped point cloud subset, performing equal-thickness layered slicing along the vertical direction, calculating the projection area layer by layer and accumulating the volume of each layer to generate the total volume of the tree crown.

[0008] Optionally, the cropped point cloud subset and / or slice distribution is rendered in real time through an interactive interface, and the processing result is exported as a standardized point cloud format file.

[0009] Optionally, feature extraction is performed based on the normal vector, curvature and covariance matrix of the neighborhood point set of each point to determine the surface features of the trunk, branches and leaves; trees and background are determined based on the shape and density of the point cloud.

[0010] Optionally, the preprocessed point cloud data is recursively divided into an octree structure, and an octree index is established; based on the octree index, the center point of each block inside the octree is extracted to generate the center point point cloud data, and the K nearest neighbor points of each center point point cloud data are searched, where K is a positive integer; a maximum neighborhood distance threshold is set, and points that meet the conditions are clustered into the same connected domain; a unique color label is assigned to each connected domain, and a visual segmentation point cloud result is generated.

[0011] Optionally, an initial cropping frame is defined in a three-dimensional space; an affine transformation matrix is ​​generated according to a user input, and a translation, scaling or rotation operation is performed on the initial cropping frame based on the affine transformation matrix.

[0012] Optionally, set slice parameters; the slice parameters include slice thickness and inter-layer gap; assign point clouds to corresponding slice layers based on the set slice parameters; perform two-dimensional projection on each slice layer, and calculate the area and cumulative volume.

[0013] Optionally, the cropped point cloud subset and / or slice distribution is rendered in real time through an interactive interface, including at least one of the following: updating and displaying the cropping results and / or slice distribution in real time according to the user's adjustment action on the 3D cropping frame; assigning color identification according to independent labels; setting transparency gradient according to slice layers.

[0014] Optionally, the slice layer number to which the point belongs is determined based on the height of the point, the height of the lowest point of the cropping box, the slice thickness and the inter-layer gap height; if the absolute value of the difference between the height of the point and the minimum height of the generated slice layer is less than or equal to the slice thickness, the point belongs to the corresponding slice layer.

[0015] Optionally, if the absolute value of the difference between the height of the point and the minimum height of the generated slice layer is greater than the slice thickness, the point belongs to the gap.

[0016] In a second aspect, the present invention provides an analysis system for tree crown layers based on point cloud data, the analysis system comprising: a data acquisition module, a preprocessing module, a monocular segmentation module, a cropping module, a slicing module and an area and volume calculation module; the data acquisition module is used to acquire an original point cloud data set of forestry resources; the preprocessing module is used to preprocess the original point cloud data set acquired by the data acquisition module to obtain a preprocessed point cloud data set; the monocular segmentation module is used to construct an octree index partition space based on the preprocessed point cloud data set processed by the preprocessing module, and use a connectivity clustering algorithm to separate individual trees to generate a point cloud segmentation result with labels; the cropping module is used to generate a 3D cropping frame in real time according to user input when there is a need to analyze a single tree, and based on the 3D cropping frame and the single tree segmented by the monocular segmentation module, screen and output a cropped point cloud subset of the target area; the slicing module is used to crop the point cloud subset based on the cropping module, and perform equal-thickness layered slicing along the vertical direction; the area and volume calculation module is used to calculate the projection area layer by layer and accumulate the volume of each layer to generate the total volume of the tree crown.

[0017] Optionally, the analysis system also includes: a display module and a file export module; the display module is used to render the cropped point cloud subset and / or slice distribution in real time through an interactive interface; the file export module is used to export the processing results into a standardized point cloud format file.

[0018] Optionally, the monocular segmentation module is specifically used to: extract features based on the normal vector, curvature and covariance matrix of the neighborhood point set of each point to determine the surface features of the trunk, branches and leaves; determine the trees and background based on the shape and density of the point cloud.

[0019] Optionally, the monocular segmentation module is specifically used to: recursively divide the preprocessed point cloud data into an octree structure and establish an octree index; based on the octree index, extract the center point of each block inside the octree to generate center point point cloud data, and search for the K nearest neighbor points of each center point point cloud data, where K is a positive integer; set the maximum neighborhood distance threshold, and cluster the points that meet the conditions into the same connected domain; assign a unique color label to each connected domain to generate a visual segmentation point cloud result.

[0020] Optionally, the cropping module is specifically used to: define an initial cropping frame in a three-dimensional space; generate an affine transformation matrix according to a user input, and perform translation, scaling or rotation operations on the initial cropping frame based on the affine transformation matrix.

[0021] Optionally, the slicing module is specifically used to: set slicing parameters; the slicing parameters include slice thickness, interlayer gap, and allocate point clouds to corresponding slicing layers based on the set slicing parameters; the area and volume calculation module is specifically used to: perform two-dimensional projection on each slice layer, and calculate the area and cumulative volume.

[0022] Optionally, the display module is specifically used to: update and display the cropping result and slice distribution in real time according to the user's adjustment action on the 3D cropping frame; assign color identification according to independent labels; and set transparency gradient according to slice layers.

[0023] Optionally, the slicing module is specifically used to: determine the slicing layer to which the point belongs based on the height of the point, the height of the lowest point of the cropping box, the slice thickness and the inter-layer gap height; if the absolute value of the difference between the height of the point and the minimum height of the generated slicing layer is less than or equal to the slice thickness, the point belongs to the corresponding slicing layer.

[0024] Optionally, the slicing module is specifically configured to: if the absolute value of the difference between the height of the point and the minimum height of the generated slicing layer is greater than the slice thickness, the point belongs to the gap.

[0025] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for analyzing tree crowns based on point cloud data as described in the first aspect is implemented.

[0026] According to a fourth aspect of an embodiment of the present invention, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the computer-readable instructions, when executed by the processor, implement the tree canopy analysis method based on point cloud data as described in the first aspect.

[0027] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0028] In the embodiment of the present invention, first, the original point cloud data set of forestry resources can be obtained, and the original point cloud data set can be preprocessed. Then, based on the preprocessed point cloud data set, the space is divided by constructing an octree index, and then the connectivity distance algorithm is used to separate individual trees from the point cloud data set, and a point cloud segmentation result with labels is generated. Tree parameters can be accurately extracted based on the point cloud segmentation result, and a structured parameter table can be generated. In the case of analyzing a single tree, a 3D cropping frame with a freely changeable shape can be generated based on the user's input, and the single tree can be layered and sliced ​​based on the 3D cropping frame. Then, the projection area of ​​each layer after slicing is calculated layer by layer and the volume of each layer is accumulated, so that the total volume of the crown can be accurately generated. Compared with the traditional analysis method, the speed and efficiency of extracting areas of interest (such as single trees, tree groups or terrain in a specific area) from large-scale forest point cloud data are improved, and the accuracy and efficiency of forestry surveys are improved.

[0029] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of the architecture of a tree crown layer analysis system based on point cloud data provided in an embodiment of the present invention.

[0031] Figure 2 A schematic flow chart of a method for analyzing tree crown layers based on point cloud data provided in an embodiment of the present invention.

[0032] Figure 3 A schematic diagram of a visual display of forestry point cloud data provided by an embodiment of the present invention.

[0033] Figure 4 Another schematic diagram of visual display of forestry point cloud data provided by an embodiment of the present invention.

[0034] Figure 5 Another schematic diagram of visual display of forestry point cloud data provided by an embodiment of the present invention.

[0035] Figure 6 A schematic diagram of an interactive 3D cropping frame provided by an embodiment of the present invention.

[0036] Figure 7 A schematic diagram of point cloud data of a single tree after monocular segmentation provided by an embodiment of the present invention.

[0037] Figure 8 A schematic diagram of an interface for setting slice parameters provided in an embodiment of the present invention.

[0038] Fig. 9 A schematic diagram of determining a slice based on a 3D selection box provided by an embodiment of the present invention.

[0039] Fig.10 A hardware structure diagram of a computer device in which a tree canopy analysis system based on point cloud data is located in an embodiment of the present invention.

[0040] Fig.11 A schematic diagram of the hardware structure of a tree canopy analysis system based on point cloud data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0042] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0043] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0044] Next, the embodiments of the present invention are described in detail.

[0045] Figure 1 A tree crown layer analysis system architecture based on point cloud data is provided in an embodiment of the present invention, such as Figure 1 As shown, the system architecture 100 may include one or more of terminal devices such as a smart phone 101, a portable computer 102, a desktop computer 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0046] The terminal device can be any electronic device with data processing function, which has a display screen for displaying point cloud data sets, 3D cropping boxes, slices, etc. to the user. The electronic device includes but is not limited to the above-mentioned desktop computers, portable computers, smart phones, tablet computers, etc.

[0047] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0048] The method provided in the embodiment of the present invention can be executed by a terminal device, and accordingly, the device can be set in the terminal device. However, it is easy for those skilled in the art to understand that the tree crown canopy layer analysis method based on point cloud data provided in the embodiment of the present invention can also be executed by a server, and accordingly, the device can also be set in the server, which is not particularly limited in this exemplary embodiment.

[0049] Figure 2 A schematic diagram of a method for analyzing tree crown layers based on point cloud data provided by an embodiment of the present invention. Figure 2 As shown in , the method includes the following S201 to S206:

[0050] S201. Obtain an original point cloud dataset of forestry resources.

[0051] Exemplarily, a three-dimensional scan of a forestry area is performed using a lidar device to generate an original point cloud dataset including tree surface geometric information.

[0052] It should be noted that the original point cloud dataset is composed of a large number of three-dimensional points, each point is represented by (x, y, z, a1, a2, ...).

[0053] Among them, "x, y, z" are spatial coordinates; "a1, a2, ..." are additional attributes; for example: reflection intensity (Intensity), color value (RGB, Red Green Blue), timestamp, etc.

[0054] It should be noted that after obtaining the original point cloud dataset, the point cloud dataset can be loaded through the visualization module.

[0055] Exemplarily, in the embodiment of the present invention, a visualization module of a software platform that uses an octree structure internally may be selected to load point cloud data in different formats.

[0056] Optionally, the point cloud data may be in a data format such as LAZ, LAS, XYZ, or E57.

[0057] Figure 3 A schematic diagram of a visual display of forestry point cloud data provided by an embodiment of the present invention. Figure 4 Another schematic diagram of visual display of forestry point cloud data provided by an embodiment of the present invention. Figure 3 The forestry point cloud data shown in the figure is taken. Figure 4 Yes Figure 3 Forestry point cloud data after the image is rotated by an angle.

[0058] S202: pre-process the original point cloud data set to obtain a pre-processed point cloud data set.

[0059] The preprocessing includes at least one of the following: denoising, filtering and normal estimation operations.

[0060] It should be noted that before performing tree segmentation, the collected original point cloud data set can be preprocessed to improve the accuracy and efficiency of segmentation.

[0061] For example, a statistical filter algorithm (SOR filter) or a low-pass filter algorithm (Noise filter) can be used to remove noise points in the original point cloud dataset. The original point cloud dataset is downsampled and smoothed using filters in PCL (Point Cloud Library).

[0062] S203, based on the preprocessed point cloud data set, construct an octree index to divide the space, and use a connectivity clustering algorithm to separate individual trees to generate a point cloud segmentation result with labels.

[0063] It should be noted that by establishing a spatial index structure for point cloud data, quick query and cropping can be achieved in subsequent steps.

[0064] In an embodiment of the present invention, when processing large-scale dense point cloud data, an octree algorithm structure is used as an index optimization structure of the point cloud.

[0065] Specifically, the point cloud space is recursively divided into eight subspaces, and each subspace is cyclically recursive to obtain the final structure. When cropping is performed based on the 3D cropping box, points outside the 3D cropping box can be quickly removed to improve the cropping efficiency.

[0066] It can be understood that in this step, each tree can be identified and distinguished by different colors to perform monocular segmentation of the trees.

[0067] Specifically, ground point extraction, elevation normalization, trunk extraction, and DBH point extraction can be performed based on the preprocessed point cloud dataset to achieve monocular segmentation, thereby obtaining information about each tree.

[0068] It should be noted that the clustering method based on distance calculation between objects in the entire data set is called connectivity-based clustering or hierarchical clustering algorithm.

[0069] S204: extract tree parameters based on each tree segmentation data in the point cloud segmentation result, and generate a structured parameter table.

[0070] The tree parameters include at least one of the diameter at breast height, height, and trunk diameter of each tree.

[0071] It can be understood that tree information statistics can be performed in this step.

[0072] Exemplarily, the data of each tree may be split into cubic blocks based on point cloud attributes, wherein a block may be represented by a center point and eight vertices.

[0073] The tree information can be obtained based on the 8 vertex information, the center point and the point cloud data contained in the cube.

[0074] Figure 5 Another schematic diagram of visual display of forestry point cloud data provided by an embodiment of the present invention.

[0075] in, Figure 5 Based on Figure 3 The schematic diagram of the point cloud data after monocular segmentation is shown. Figure 5 Middle for Figure 3 A bird's-eye view of the trees after monocular segmentation. Different colors represent different heights. After segmentation, the system automatically numbers different trees and generates tree parameters for each data based on the segmentation data of each tree.

[0076] Table 1 is an exemplary table of tree information provided by an embodiment of the present invention. The data in Table 1 are based on Figure 5 The point cloud data shown in the figure is segmented monocularly and the table of tree parameters is obtained by extracting. Table 1 takes the tree ID (identity document, ID), X coordinate, Y coordinate, tree height, diameter at breast height (Diameter at Breast Height, DBH), crown length (maximum horizontal extension of the crown), north-south crown length (maximum horizontal extension of the crown from due north to due south), east-west crown length (maximum horizontal extension of the crown from due east to due west), crown area and other information as examples for explanation.

[0077] Table 1

[0078] S205: When there is a need to analyze a single tree, a 3D cropping frame is generated in real time according to the user's input, and a cropped point cloud subset of the target area is screened and output based on the 3D cropping frame and the single tree.

[0079] Among them, the target area is the region of interest (ROI).

[0080] Optionally, in an embodiment of the present invention, the 3D cropping box is a three-dimensional collection area, which can be adjusted according to user needs and is used to define the interest range of the point cloud data to facilitate the extraction of the target area.

[0081] Figure 6 A schematic diagram of an interactive 3D cropping frame provided by an embodiment of the present invention, such as Figure 6As shown in the figure, the initial coordinates of the 3D cropping frame are (13.9889937, 107.01799774, 29.85741425). The 3D cropping frame can be adjusted according to the arrows in different directions. In the visual interface, users can select a tree from the point cloud data and adjust the size and angle of the 3D cropping frame to accurately crop individual trees. Figure 7 The cropping results of the individual plants shown.

[0082] It should be noted that, in the interactive interface provided by the embodiment of the present invention, a vertex of the 3D cropping box is selected to be placed at the origin of the three-dimensional coordinate axis, and the coordinates of each vertex of the 3D cropping box and the length, width and height of the 3D cropping box are represented by the distance between other vertices and the origin. Similarly, a single tree after monocular segmentation is displayed based on the origin.

[0083] It should be noted that, during the cropping process, the user can select a cropping range on a two-dimensional plane, and the point cloud actually cropped is the point cloud in the three-dimensional space within the cropping range.

[0084] Optionally, in the embodiment of the present invention, the 3D cropping frame can perform internal cropping and external cropping.

[0085] Among them, the internal clipping indicates clipping the inside of the tree, and the external clipping indicates clipping the outside of a single tree.

[0086] It can be understood that the point cloud in the target area (ie, the area indicated by the geometric parameters) can be quickly extracted based on the geometric parameters of the 3D cropping box.

[0087] S206, based on the cropped point cloud subset, perform equal-thickness layered slicing along the vertical direction, calculate the projection area layer by layer and accumulate the volume of each layer to generate the total volume of the crown.

[0088] Optionally, in an embodiment of the present invention, during the tree crown canopy analysis process based on point cloud data, the 3D cropping frame and the loaded point cloud data can be displayed in real time on the interactive interface, as well as the adjustment process of the 3D cropping frame based on user operations such as free rotation and scaling, and the results after cropping based on the 3D cropping frame can be visualized in real time.

[0089] The embodiment of the present invention provides a tree crown layer analysis method based on point cloud data. First, the original point cloud data set of forestry resources can be obtained, and the original point cloud data set can be preprocessed. Then, based on the preprocessed point cloud data set, the space is divided by constructing an octree index, and then the connectivity distance algorithm is used to separate individual trees from the point cloud data set, and a point cloud segmentation result with labels is generated. Tree parameters can be accurately extracted based on the point cloud segmentation result, and a structured parameter table can be generated. In the case of analyzing a single tree, a 3D cropping frame with a freely changeable shape can be generated based on the user's input, and the single tree can be sliced ​​in layers based on the 3D cropping frame. Then, the projection area of ​​each layer after slicing is calculated layer by layer and the volume of each layer is accumulated, so that the total volume of the tree crown can be accurately generated. Compared with the traditional analysis method, the speed and efficiency of extracting areas of interest (such as single trees, tree groups or terrain in a specific area) from large-scale forest point cloud data are improved, and the accuracy and efficiency of forestry surveys are improved.

[0090] Optionally, the tree crown layer analysis method based on point cloud data provided in the embodiment of the present invention may further include the following S207:

[0091] S207 , rendering and cropping point cloud subsets and / or slice distributions in real time through an interactive interface, and exporting the processing results into a standardized point cloud format file.

[0092] It should be noted that, in an embodiment of the present invention, after obtaining the original point cloud data set of forestry resources, the point cloud data can be displayed through an interactive interface, and before cropping based on a 3D cropping frame, a single tree can be displayed in real time. When a single tree is cropped based on a 3D cropping frame, the obtained point cloud subset and slice distribution can be displayed through the interactive interface.

[0093] It should be noted that after cropping, the cropping result can be exported in a standardized point cloud format file, for example, a file in LAS or E57 format.

[0094] Specifically, in the tree crown layer analysis method based on point cloud data provided by the embodiment of the present invention, the above S203 may specifically include the following S203a and S203b:

[0095] S203a, performing feature extraction based on the normal vector, curvature and covariance matrix of the neighborhood point set of each point to determine the surface features of the trunk, branches and leaves.

[0096] S203b, determining trees and background based on the shape and density of the point cloud.

[0097] For example, by calculating the normal vector of each point, points on different surfaces can be distinguished. Trunks and branches usually have relatively consistent normal vectors, while leaves are more scattered; points with higher curvature are usually located at the edges of leaves or branches; the neighborhood point set of points on the trunk or stem is usually face-shaped (cylindrical), while the leaf point cloud is more scattered.

[0098] Based on this scheme, feature extraction can be performed during the tree canopy analysis based on point cloud data to determine which feature of the tree the point corresponds to, thereby facilitating the distinction between the tree and the background and determining the trunk, branches, and leaves of the tree.

[0099] Optionally, in the tree crown layer analysis method based on point cloud data provided in an embodiment of the present invention, the above S203 may specifically include the following S203c to S203f:

[0100] S203c, recursively divide the preprocessed point cloud data into an octree structure, and establish an octree index.

[0101] S203d, based on the octree index, extract the center point of each block inside the octree to generate center point point cloud data, and search for K nearest neighbor points of each center point point cloud data.

[0102] The K nearest neighbor points can be determined based on the K-Nearest Neighbors (KNN) algorithm, where K is a positive integer.

[0103] S203e: Set a maximum neighborhood distance threshold and cluster the points that meet the conditions into the same connected domain.

[0104] Specifically, the above steps can be repeated on the unlabeled point cloud through recursive iteration to achieve label connected domain clustering.

[0105] It should be noted that the points that meet the conditions are those whose distance from the center point is equal to or less than the maximum neighborhood distance threshold.

[0106] S203f, assign a unique color label to each connected domain and generate a visual segmentation point cloud result.

[0107] Based on this solution, by establishing an octree index for the point cloud data, point cloud segmentation can be quickly performed based on the octree index and clustering algorithm. Different color labels are given to different connected domains, and different point cloud segmentation results can be visually distinguished and displayed.

[0108] Optionally, in the tree crown layer analysis method based on point cloud data provided in an embodiment of the present invention, the above S205 may specifically include the following S205a to S205c:

[0109] S205a: Define an initial cropping frame in three-dimensional space.

[0110] Exemplarily, the point cloud range is limited by six planes to form an axis-aligned cuboid.

[0111] Point p(x, y, z) satisfies the clipping condition: x min ≤x≤x max ,y min ≤y≤y max , z min ≤z≤z max .

[0112] In the embodiment of the present invention, the initial size of the 3D cropping frame can be constructed by taking the origin of the point cloud coordinate system as the center and the maximum values ​​corresponding to the three axes as the boundaries of the initial cropping frame.

[0113] Right now:

[0114] P0 (0, 0, 0), P1 (X.max, Y.max, Z.min), P2 (X.max, Y.min, Z.min), P3 (X.max, Y.max, Z.min ), P4 (X.min, Y.max, Z.min), P5 (X.max, Y.min, Z.max), P6 (X.min, Y.min, Z.min), P7 (X.max, Y.max, Z.max), P8 (X.min, Y.min, Z.max).

[0115] S205b: Generate an affine transformation matrix according to the user's input, and perform translation, scaling or rotation operations on the initial cropping frame based on the affine transformation matrix.

[0116] Among them, for the translation operation, the affine transformation includes the vector Along The coordinates after translation adjustment are: .

[0117] For scaling operations, multiply the 3D vector by the affine transformation matrix (scaling factor) Control size, where Indicates the scaling factor of the x-axis, Indicates the scaling factor of the y-axis, Indicates the scaling factor of the z-axis.

[0118] For rotation operations, the rotation of an object is determined by its posture, that is, its rotation angle around the x-axis (roll), the rotation angle around the y-axis (pitch), and the rotation angle around the z-axis (yaw).

[0119] The affine transformation matrix can be Control the rotation.

[0120] Assuming that the cropping frame is rotated around the y-axis, x-axis, and z-axis by angles α, β, and γ, the rotation transformation matrix around the y-axis is , the rotation transformation matrix around the x-axis is , the rotation transformation matrix around the z-axis is .

[0121] It should be noted that if the user input for adjusting the position, size, and angle of the cropping frame in the interface is detected, the point set in the frame can be recalculated in real time and displayed visually.

[0122] Optionally, when adjusting the 3D cropping frame parameters in real time, a cache technology may be used to avoid repeated calculations.

[0123] Optionally, the user's adjustment input may be input by dragging, selecting, translating, etc. with a mouse, or may be input by directly inputting X, Y, and Z parameters, which is not specifically limited in the embodiment of the present invention.

[0124] Optionally, in the tree crown layer analysis method based on point cloud data provided in the embodiment of the present invention, after the above S205b, the following S205c may be further included:

[0125] S205c, eliminating point clouds outside the 3D cropping box based on the octree index.

[0126] Specifically, the screening process can be accelerated through parallel computing using a graphics processing unit (GPU).

[0127] Exemplarily, an octree is used to quickly find a point set that intersects with the range of a 3D clipping box, indexed leaf nodes store the spatial positions of the points, and nodes outside the selection are directly discarded.

[0128] It should be noted that for large-scale point cloud data, the cropping process of the cropping box can be improved in efficiency through parallelization.

[0129] (1) Block cropping: divide the point cloud into several sub-regions and perform cropping calculations independently;

[0130] (2) GPU acceleration: CUDA (Compute Unified Device Architecture, a computing platform) is used for parallel computing to process the judgment of inside and outside the point cloud cropping box.

[0131] Optionally, during the cropping process based on the 3D cropping frame, the cropping results can be displayed visually, and the interactive experience can be improved by instantly displaying the cropped point cloud; the cropping area can be displayed in real time for user judgment; and the cropping area can also be "cloned" and stored independently to maintain the integrity of the original data.

[0132] It should be noted that the tree crown and layer analysis method based on point cloud data provided in an embodiment of the present invention can provide a parameterized automatic slicing function, such as initializing the slice cropping thickness, fixing the axial cropping, performing equal-interval cropping based on the cropping spacing (for example, by adding a constant a to any axial direction (x, y, z) of the current cropping frame, and achieving equal-interval multi-level cropping through the same axial value + a of the cropping frame), randomly displaying slice colors, extracting cross-section lines, etc.

[0133] Optionally, in the tree crown layer analysis method based on point cloud data provided in an embodiment of the present invention, the above S206 may specifically include the following S206a to S206c:

[0134] S206a, setting slice parameters.

[0135] Among them, the slice parameters include slice thickness and interlayer gap.

[0136] Figure 7 A schematic diagram of point cloud data of a single tree after monocular segmentation provided by an embodiment of the present invention. Figure 7 As shown in the figure, the interactive interface displays the visualized point cloud data of a single tree with coordinates (4.07099915, 3.37699890, 9.03866673) obtained through monocular segmentation.

[0137] Figure 8 A schematic diagram of an interface for setting slice parameters provided in an embodiment of the present invention. The user can rotate the point cloud data of a single tree after segmentation around a fixed axis in the left figure and input slice parameters on the right. The parameters of multiple slices can be set in the interactive interface, and the direction of the slices can be selected, such as slicing on the X-axis, the Y-axis, or the Z-axis. The parameters of the slice gap, slice color, and extracted contour (i.e., the contour of the slice) can be set in the interactive interface.

[0138] S206b, allocating the point cloud to the corresponding slice layer based on the set slice parameters.

[0139] For example, it can be based on Assign point clouds to corresponding slice layers.

[0140] Where k represents the number of slice layers, Indicates the height of the point, Indicates the height of the lowest point of the cropping box. represents the slice thickness, Indicates the interlayer gap height.

[0141] Optionally, based on the height of the point, the height of the lowest point of the cropping frame, the slice thickness and the inter-layer gap height, the slice layer number to which the point belongs is determined; if the absolute value of the difference between the height of the point and the minimum height of the generated slice layer is less than or equal to the slice thickness, the point belongs to the corresponding slice layer; if the absolute value of the difference between the height of the point and the minimum height of the generated slice layer is greater than the slice thickness, the point belongs to the gap.

[0142] S206c, perform two-dimensional projection on each slice, and calculate the area and cumulative volume.

[0143] Specifically, the projection area of ​​each layer may be calculated, and the cumulative volume may be determined based on the projection area of ​​each layer, thereby obtaining the total volume of the tree crown.

[0144] Example:

[0145] Step 1: Calculate the minimum enclosing rectangle of the tree and obtain the coordinates of the lower left corner (MinCorner) and the upper right corner (MaxCorner) of the minimum enclosing rectangle.

[0146] Step 2: Calculate the number of slices

[0147] The number of slices below the crop box = (MinCorner.Z-crop box lowest point.Z) / (slice thickness + slice gap);

[0148] The number of slices above the crop box = (MaxCorner.Z-crop box lowest point.Z) / (slice thickness + slice gap);

[0149] The final number of slices N = the absolute value of the number of slices below the cropping box + the absolute value of the number of slices above the cropping box.

[0150] Create N empty point clouds according to the number of slices.

[0151] Step 3: Calculate the slice layer to which each point (P) in the tree point cloud belongs

[0152] Among them, SliceIndex = (PZ-the lowest point of the cropping box.Z) / (slice thickness + slice gap), and SliceIndex is rounded down to get the slice layer to which it belongs.

[0153] Step 4: Determine whether the point (P) is in the gap

[0154] For example, if the height of a point (P) satisfies , then the point belongs to the slice; if not, then the point belongs to the gap.

[0155] Specifically, first calculate the minimum height of the generated slice layer: MinHeight = the lowest point of the cropping box.Z + SliceIndex * (slice thickness + slice gap); if the absolute value of (PZ-MinHeight) is less than or equal to the slice thickness, the point belongs to the slice, otherwise, the point belongs to the gap; if the point belongs to the slice, add the point to the empty point cloud created in the second step according to SliceIndex.

[0156] Fig. 9 A schematic diagram of determining a slice based on a 3D selection box provided by an embodiment of the present invention, such as Fig. 9 As shown in the figure, the interactive interface shows the point cloud data of a single tree with coordinates (4.07099915, 3.37699890, 9.03866577) after being sliced ​​on the Z axis with a thickness of 0.100. Different layers of slices are distinguished by different colors, and the volume of the tree is shown to be 29.240.

[0157] Based on this scheme, the point cloud data of a single tree can be accurately sliced, and the point cloud data after slice processing can be projected two-dimensionally. When the slice is thin enough, the area of ​​each slice is measured. Finally, the areas of enough slices are accumulated, so that the crown area and crown volume of the tree can be accurately obtained.

[0158] Optionally, in the tree crown layer analysis method based on point cloud data provided by an embodiment of the present invention, the above S207 may include at least one of the following S207a to S207c:

[0159] S207a: According to the user's adjustment action on the 3D cropping frame, the cropping result and / or slice distribution is updated and displayed in real time.

[0160] The adjustment action may include dragging, rotating and scaling the cropping frame.

[0161] S207b, assigning color identifications according to independent labels.

[0162] S207c. Set the transparency gradient according to the slice layer.

[0163] Based on this solution, when analyzing the tree crown and canopy layer of point cloud data, the cropping results and slice distribution can be displayed in real time. Different trees can be distinguished by different colors, and different slice layers can be distinguished in the display interface by different transparencies.

[0164] Corresponding to the above-mentioned method embodiments, the present invention also provides embodiments of an apparatus and a computer device to which the apparatus is applied.

[0165] The embodiments of the device of the present invention can be applied to computer devices, such as servers or terminal devices. The device embodiments can be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, a device in a logical sense is formed by the processor of the tree canopy analysis system based on point cloud data reading the corresponding computer program instructions in the non-volatile memory into the memory and running them. From the hardware level, if Fig.10 FIG. 1 is a hardware structure diagram of a computer device where the device of the embodiment of the present invention is located, except Fig.10 In addition to the processor 1010, memory 1030, network interface 1020, and non-volatile memory 1040 shown, the server or electronic device where the tree canopy analysis system 1031 based on point cloud data in the embodiment is located may also include other hardware, usually according to the actual function of the computer device, which will not be described in detail.

[0166] Fig.11 A schematic diagram of the hardware structure of a tree canopy analysis system based on point cloud data provided by an embodiment of the present invention is shown in FIG. Fig.11 As shown, the tree crown canopy analysis system 1100 based on point cloud data includes: a data acquisition module 1101, a preprocessing module 1102, a monocular segmentation module 1103, a cropping module 1104, a slicing module 1105 and an area volume calculation module 1106; the data acquisition module 1101 is used to acquire the original point cloud data set of forestry resources; the preprocessing module 1102 is used to preprocess the original point cloud data set acquired by the data acquisition module to obtain a preprocessed point cloud data set; the monocular segmentation module 1103 is used to construct an octree based on the preprocessed point cloud data set processed by the preprocessing module The index divides the space and uses the connectivity clustering algorithm to separate individual trees to generate a point cloud segmentation result with labels; the cropping module 1104 is used to generate a 3D cropping frame in real time according to the user's input when there is a need to analyze a single tree, and based on the 3D cropping frame and the single tree segmented by the monocular segmentation module, screen and output the cropped point cloud subset of the target area; the slicing module 1105 is used to crop the point cloud subset based on the cropping module and perform equal-thickness layered slicing in the vertical direction; the area and volume calculation module 1106 is used to calculate the projection area layer by layer and accumulate the volume of each layer to generate the total volume of the crown.

[0167] Optionally, the tree crown canopy analysis system based on point cloud data also includes: a display module and a file export module; the display module is used to render the cropped point cloud subset and / or slice distribution in real time through an interactive interface; the file export module is used to export the processing results into a standardized point cloud format file.

[0168] Optionally, the monocular segmentation module is specifically used to: extract features based on the normal vector, curvature and covariance matrix of the neighborhood point set of each point to determine the surface features of the trunk, branches and leaves; determine the trees and background based on the shape and density of the point cloud.

[0169] Optionally, the monocular segmentation module is specifically used to: recursively divide the preprocessed point cloud data into an octree structure and establish an octree index; based on the octree index, extract the center point of each block inside the octree to generate the center point point cloud data, and search for the K nearest neighbor points of each center point point cloud data; set the maximum neighborhood distance threshold, and cluster the points that meet the conditions into the same connected domain; assign a unique color label to each connected domain to generate a visual segmentation point cloud result.

[0170] Optionally, the cropping module is specifically used to: define an initial cropping frame in a three-dimensional space; generate an affine transformation matrix according to a user input, and perform translation, scaling or rotation operations on the initial cropping frame based on the affine transformation matrix.

[0171] Optionally, the slicing module is specifically used to: set slicing parameters; the slicing parameters include slice thickness, interlayer gap, and allocate point clouds to corresponding slicing layers based on the set slicing parameters; the area and volume calculation module is specifically used to: perform two-dimensional projection on each slice layer, and calculate the area and cumulative volume of each slice layer.

[0172] Optionally, the display module is specifically used to: update and display the cropping result and slice distribution in real time according to the user's adjustment action on the 3D cropping frame; assign color identification according to independent labels; and set transparency gradient according to slice layers.

[0173] Optionally, the slicing module is specifically used to: determine the slicing layer to which the point belongs based on the height of the point, the height of the lowest point of the cropping box, the slice thickness and the inter-layer gap height; if the absolute value of the difference between the height of the point and the minimum height of the generated slicing layer is less than or equal to the slice thickness, the point belongs to the corresponding slicing layer.

[0174] Optionally, the slicing module is specifically configured to: if the absolute value of the difference between the height of the point and the minimum height of the generated slicing layer is greater than the slice thickness, the point belongs to the gap.

[0175] The embodiment of the present invention provides an analysis system for tree crowns based on point cloud data. First, the original point cloud data set of forestry resources can be obtained, and the original point cloud data set can be preprocessed. Then, based on the preprocessed point cloud data set, the space is divided by constructing an octree index, and then a connectivity distance algorithm is used to separate individual trees from the point cloud data set, and a point cloud segmentation result with labels is generated. Tree parameters can be accurately extracted based on the point cloud segmentation result, and a structured parameter table can be generated. In the case of analyzing a single tree, a 3D cropping frame with a freely changeable shape can be generated based on the user's input, and a single tree can be sliced ​​in layers based on the 3D cropping frame. Then, the projection area of ​​each layer after slicing is calculated layer by layer and the volume of each layer is accumulated, so that the total volume of the tree crown can be accurately generated. Compared with the traditional analysis method, the speed and efficiency of extracting areas of interest (such as single trees, tree groups or terrain in a specific area) from large-scale forest point cloud data are improved, and the accuracy and efficiency of forestry surveys are improved.

[0176] Correspondingly, the present invention also provides a tree crown canopy layer analysis system based on point cloud data, the system includes a processor; a memory for storing processor executable instructions; wherein the processor is configured to: obtain an original point cloud data set of forestry resources; preprocess the original point cloud data set to obtain a preprocessed point cloud data set; based on the preprocessed point cloud data set, construct an octree index partition space, and use a connectivity clustering algorithm to separate individual trees to generate a point cloud segmentation result with labels; based on each tree segmentation data in the point cloud segmentation result, extract tree parameters and generate a structured parameter table; the tree parameters include at least one of the diameter at breast height, height, and trunk diameter of each tree; when there is a need to analyze a single tree, a 3D cropping frame is generated in real time according to the user's input, and based on the 3D cropping frame and the single tree, a cropped point cloud subset of the target area is screened and output; based on the cropped point cloud subset, equal-thickness layered slices are performed along the vertical direction, the projection area is calculated layer by layer and the volume of each layer is accumulated to generate the total volume of the tree crown.

[0177] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps in the above-mentioned method for analyzing tree crowns and canopies based on point cloud data are implemented.

[0178] The present invention also provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the computer-readable instructions, when executed by the processor, implement the various steps in the above-mentioned method embodiment for analyzing tree crowns based on point cloud data.

[0179] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

[0180] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the modules determined as separated components may or may not be physically separated, and the components determined as module display may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Those of ordinary skill in the art can understand and implement it without paying creative labor.

[0181] The above describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0182] Those skilled in the art will readily appreciate other embodiments of the invention after considering the specification and practicing the invention claimed herein. The invention is intended to cover any variations, uses or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not claimed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0183] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0184] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing tree canopy layers based on point cloud data, characterized in that: The method comprises: Obtain original point cloud datasets of forestry resources; Preprocessing the original point cloud data set to obtain a preprocessed point cloud data set; Based on the pre-processed point cloud data set, an octree index is constructed to divide the space, and a connectivity clustering algorithm is used to separate individual trees to generate a point cloud segmentation result with labels; Extracting tree parameters based on each tree segmentation data in the point cloud segmentation result and generating a structured parameter table; the tree parameters include at least one of the diameter at breast height, height, and trunk diameter of each tree; In the case of a single tree analysis requirement, a 3D cropping frame is generated in real time according to the user's input, and a cropped point cloud subset of the target area is screened and output based on the 3D cropping frame and the single tree; Based on the cropped point cloud subset, equal-thickness layered slices are performed along the vertical direction, the projection area is calculated layer by layer, and the volume of each layer is accumulated to generate the total volume of the tree crown.

2. The method according to claim 1, characterized in that The method further comprises: The cropped point cloud subset and / or slice distribution is rendered in real time through an interactive interface, and the processing result is exported as a standardized point cloud format file.

3. The method according to claim 1, characterized in that The method of constructing an octree index partition space based on the preprocessed point cloud data set and using a connectivity clustering algorithm to separate individual trees to generate a point cloud segmentation result with labels includes: Feature extraction is performed based on the normal vector, curvature and covariance matrix of the neighborhood point set of each point to determine the surface features of the trunk, branches and leaves; Based on the shape and density of the point cloud, trees and background are identified.

4. The method according to claim 3, characterized in that The method of constructing an octree index partition space based on the preprocessed point cloud data set and using a connectivity clustering algorithm to separate individual trees to generate a point cloud segmentation result with labels includes: Recursively dividing the preprocessed point cloud data into an octree structure and establishing an octree index; Based on the octree index, extract the center point of each block in the octree to generate center point cloud data, and search for K nearest neighbor points of each center point cloud data, where K is a positive integer; Set the maximum neighborhood distance threshold and cluster the points that meet the conditions into the same connected domain; Assign a unique color label to each connected domain and generate a visual segmentation point cloud result.

5. The method according to claim 1, characterized in that Generating a 3D cropping frame in real time according to the user's input includes: Define an initial cropping box in 3D space; An affine transformation matrix is ​​generated according to the user's input, and the initial cropping frame is translated, scaled or rotated based on the affine transformation matrix.

6. The method according to claim 1, characterized in that Based on the cropped point cloud subset, equal thickness slicing is performed along the vertical direction, the projection area is calculated layer by layer and the volume of each layer is accumulated to generate the total volume of the tree crown, including: Setting slice parameters; the slice parameters include slice thickness and interlayer gap; Assign point clouds to corresponding slice layers based on the set slice parameters; Each slice was projected two-dimensionally, and the area and cumulative volume were calculated.

7. The method according to claim 2, characterized in that The real-time rendering of the cropped point cloud subset and / or slice distribution through an interactive interface includes at least one of the following: According to the user's adjustment action on the 3D cropping frame, the cropping result and / or the slice distribution are updated and displayed in real time; Assign color codes according to independent labels; Set the transparency gradient according to the slice layer.

8. The method according to claim 6, characterized in that The allocating point clouds to corresponding slice layers based on the set slice parameters includes: Based on the height of the point, the height of the lowest point of the cropping box, the slice thickness and the height of the inter-layer gap, determine the slice layer number to which the point belongs; If the absolute value of the difference between the height of the point and the minimum height of the generated slice layer is less than or equal to the slice thickness, the point belongs to the corresponding slice layer.

9. The method according to claim 8, characterized in that The method further comprises: If the absolute value of the difference between the height of a point and the minimum height of a generated slice layer is greater than the slice thickness, the point belongs to a gap.

10. A tree canopy analysis system based on point cloud data, characterized in that: The analysis system comprises: a data acquisition module, a preprocessing module, a monocular segmentation module, a cropping module, a slicing module and an area and volume calculation module; The data acquisition module is used to acquire the original point cloud data set of forestry resources; The preprocessing module is used to preprocess the original point cloud data set acquired by the data acquisition module to obtain a preprocessed point cloud data set; The monocular segmentation module is used to construct an octree index partition space based on the preprocessed point cloud data set processed by the preprocessing module, and use a connectivity clustering algorithm to separate individual trees to generate a point cloud segmentation result with labels; The cropping module is used to generate a 3D cropping frame in real time according to the user's input when there is a need to analyze a single tree, and to filter and output a cropped point cloud subset of the target area based on the 3D cropping frame and the single tree segmented by the monocular segmentation module; The slicing module is used to perform equal-thickness layered slicing along a vertical direction based on the cropped point cloud subset by the cropping module; The area volume calculation module is used to calculate the projection area layer by layer and accumulate the volume of each layer to generate the total volume of the tree crown.

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