CN111598915A - Point cloud single wood segmentation method, device, equipment and computer readable medium - Google Patents
Point cloud single wood segmentation method, device, equipment and computer readable medium Download PDFInfo
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Abstract
The application relates to a point cloud single tree segmentation method, a point cloud single tree segmentation device, point cloud single tree segmentation equipment and a computer readable medium. The method comprises the following steps: horizontally clustering the point cloud to be processed to obtain a plurality of horizontal clusters, wherein the point cloud to be processed is point cloud data obtained by scanning a target tree by scanning equipment; extracting trunk seed points of the target trees from the plurality of horizontal clusters; constructing a path diagram in the point cloud to be processed according to the tree trunk seed points; traversing and inquiring a shortest path point from the trunk seed point in the path graph; and under the condition that the distance from the current shortest path point to the reference shortest path point adjacent to the current shortest path point is less than or equal to a distance threshold value, merging the current shortest path point to a single tree point set in which the trunk seed point is located, wherein the single tree point set represents a set of points on single trees belonging to the trunk seed point. According to the technical scheme, the influence of the intersection of the branches and the trunk among the multiple trees on the single-tree segmentation is effectively reduced, the segmentation precision is improved, and the segmentation efficiency is accelerated.
Description
Technical Field
The application relates to the technical field of surveying and mapping point cloud data processing, in particular to a point cloud single tree segmentation method, a point cloud single tree segmentation device, point cloud single tree segmentation equipment and a computer readable medium.
Background
In reverse engineering, a method of obtaining a point data set of an object appearance surface by a measuring instrument is widely applied to detection of point location information in various fields such as agriculture, forestry, ground disaster, electric power, surveying and mapping. In particular, the tree height, the breast diameter, the crown area and the crown volume can be directly obtained from point cloud data obtained by scanning trees by using scanning equipment such as ground laser radar and mobile laser radar (LiDAR), and the biomass and the carbon reserve can be estimated by using an empirical formula based on a single tree structure.
At present, a plurality of defects still exist when the point cloud data is subjected to single-tree segmentation in the related technology. The inventor finds that the existing foundation laser radar single tree segmentation method has the problems of low segmentation precision, low efficiency and the like in the research process, mainly the accuracy of tree identification is low, non-trees are easily identified as trees according to simple slice clustering, then the trees are used as seed points to grow branches and leaves, and when branch and leaf point cloud growth is carried out after trunk identification, the tree is easily influenced by surrounding trees and branches, so that over-segmentation and missing segmentation are serious, and the single tree parameter extraction precision and the single tree display effect are influenced. In the related technology, a simple shortest path algorithm is adopted for growth, however, the inventor finds that the simple shortest path growth also has the problems of low efficiency and the like in the research process, the requirements of production and the like cannot be met, and the laser point clouds with dense quantity cannot be processed.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The application provides a point cloud single tree segmentation method, a point cloud single tree segmentation device, a point cloud single tree segmentation equipment and a computer readable medium, and aims to solve the technical problems of low segmentation precision and low efficiency.
In a first aspect, the present application provides a point cloud single tree segmentation method, including: horizontally clustering the point cloud to be processed to obtain a plurality of horizontal clusters, wherein the point cloud to be processed is point cloud data obtained by scanning a target tree by scanning equipment; extracting trunk seed points of the target trees from the plurality of horizontal clusters; constructing a path diagram in the point cloud to be processed according to the tree trunk seed points; traversing and inquiring a shortest path point from the trunk seed point in the path graph; and under the condition that the distance from the current shortest path point to the reference shortest path point adjacent to the current shortest path point is less than or equal to a distance threshold value, merging the current shortest path point to a single tree point set in which the trunk seed point is located, wherein the single tree point set represents a set of points on single trees belonging to the trunk seed point.
Optionally, before performing horizontal clustering on the point cloud to be processed to obtain a plurality of horizontal clusters, the method further includes preprocessing the point cloud to be processed as follows: denoising the point cloud to be processed to obtain a denoising point cloud; classifying the noise reduction point cloud to obtain ground points and tree points; and taking the relative height of the ground point from the tree point as the canopy height of the tree point.
Optionally, performing horizontal clustering on the point cloud to be processed to obtain a plurality of horizontal clusters includes: horizontally cutting the tree points at the height of the canopy according to a preset height threshold value to obtain a plurality of tree points in a horizontal layer; and carrying out horizontal Euclidean clustering on the tree points of each horizontal layer to obtain a plurality of horizontal clusters.
Optionally, extracting the stem seed points of the target tree from the plurality of horizontal clusters comprises: calculating the gravity center point of each horizontal cluster, and adding a non-tree-trunk seed point identifier for each gravity center point; taking a gravity center point in a layer of horizontal layer closest to the ground point as a first alternative trunk seed point, and adding a trunk seed point mark for the first alternative trunk seed point; calculating first directions of N gravity points and a first alternative trunk seed point in a target horizontal layer, wherein the target horizontal layer is other horizontal layers except the horizontal layer where the first alternative trunk seed point is located; calculating a second direction between any two gravity points in the N gravity points of the target horizontal layer; under the condition that the included angle between the first direction and the ground is larger than an angle threshold value, taking a group of gravity points with the minimum included angle formed by the first direction and the second direction as second alternative trunk seed points, and adding trunk seed point marks for the second alternative trunk seed points; and under the condition that the second alternative trunk seed point meets the preset condition, taking the second alternative trunk seed point as a final trunk seed point.
Optionally, before traversing the shortest path point from the query to the trunk seed point in the path graph, creating a barycentric point set as follows: carrying out European-style clustering of three-dimensional space on tree points to obtain a plurality of spatial clusters; selecting a plurality of target clusters from the plurality of spatial clusters, wherein the target clusters are spatial clusters of which the clustering points are greater than a screening threshold value; and calculating a gravity center point set consisting of the gravity center points of the plurality of target clusters, wherein the gravity center points in the gravity center point set are sorted from large to small according to the number of the cluster points in the target cluster.
Optionally, in a case that a distance from the current shortest-path point queried in the path graph to the reference shortest-path point adjacent to the current shortest-path point is greater than a distance threshold, the method further includes re-querying the shortest-path point as follows: inquiring the nearest gravity point closest to the trunk seed point in the gravity point set; and inquiring the shortest path point closest to the trunk seed point in the target cluster corresponding to the closest gravity center point, and merging the shortest path point to the single tree point set where the trunk seed point is located.
Optionally, after merging the current shortest path point into the single tree point set where the trunk seed point is located, the method further includes: performing voxelization downsampling on the point cloud to be processed to obtain a voxel gravity center point; and constructing k-dtree for the voxel gravity center point according to the single tree segmentation relation represented by the single tree point set so as to restore the single tree mark to the point cloud to be processed according to the nearest neighbor mode.
In a second aspect, the present application provides a point cloud single wood segmentation apparatus, including: the system comprises a clustering module, a processing module and a processing module, wherein the clustering module is used for horizontally clustering point clouds to be processed to obtain a plurality of horizontal clusters, and the point clouds to be processed are point cloud data obtained by scanning a target tree by scanning equipment; the extraction module is used for extracting trunk seed points of the target trees from the plurality of horizontal clusters; the building module is used for building a path map in the point cloud to be processed according to the tree trunk seed points; the query module is used for traversing and querying the shortest path point which is away from the trunk seed point in the path graph; and the distribution module is used for merging the current shortest path point into a single tree point set where the trunk seed point is located under the condition that the distance from the current shortest path point to the reference shortest path point adjacent to the current shortest path point is less than or equal to a distance threshold, wherein the single tree point set represents a set of points on single trees belonging to the trunk seed point.
In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and the processor implements the steps of any one of the above methods when executing the computer program.
In a fourth aspect, the present application also provides a computer readable medium having program code to cause a processor to perform any of the methods of the first aspect.
Compared with the related art, the technical scheme provided by the embodiment of the application has the following advantages:
according to the method, horizontal clustering is carried out on point clouds to be processed to obtain a plurality of horizontal clusters, wherein the point clouds to be processed are point cloud data obtained by scanning a target tree through scanning equipment; extracting trunk seed points of the target trees from the plurality of horizontal clusters; constructing a path diagram in the point cloud to be processed according to the tree trunk seed points; traversing and inquiring a shortest path point from the trunk seed point in the path graph; under the condition that the distance from the current shortest path point to the reference shortest path point adjacent to the current shortest path point is smaller than or equal to the distance threshold, the current shortest path point is merged to a single tree point set where the trunk seed point is located, wherein the single tree point set represents a point cloud single tree segmentation method of a set of points on single trees belonging to the trunk seed point, the influence of branch and trunk intersection among multiple trees on single tree segmentation is effectively reduced, the segmentation precision is improved, and the segmentation efficiency is accelerated.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the technical solutions in the embodiments or related technologies of the present application, the drawings needed to be used in the description of the embodiments or related technologies will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without any creative effort.
Fig. 1 is a schematic diagram of a hardware environment of an alternative point cloud single wood segmentation method according to an embodiment of the present application;
FIG. 2 is a flow chart of an alternative point cloud single tree segmentation method provided in accordance with an embodiment of the present application;
FIG. 3 is a flow chart illustrating an alternative process for pre-processing point cloud data according to an embodiment of the present disclosure;
fig. 4 is a flow chart of an alternative trunk seed point extraction provided according to an embodiment of the present application;
fig. 5 is a block diagram of an alternative point cloud single wood segmentation apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
According to an aspect of embodiments of the present application, an embodiment of a point cloud single tree segmentation method is provided.
Alternatively, in the embodiment of the present application, the point cloud single tree segmentation method may be applied to a hardware environment formed by the terminal 101 and the server 103 as shown in fig. 1. As shown in fig. 1, a server 103 is connected to a terminal 101 through a network, which may be used to provide services for the terminal or a client installed on the terminal, and a database 105 may be provided on the server or separately from the server, and is used to provide data storage services for the server 103, and the network includes but is not limited to: wide area network, metropolitan area network, or local area network, and the terminal 101 includes but is not limited to a PC, a cell phone, a tablet computer, and the like.
A point cloud single tree segmentation method in the embodiment of the present application (that is, the point cloud segmentation method in the present application is actually a fast point cloud single tree segmentation method based on shortest path and euclidean clustering algorithm) may be executed by the server 103, as shown in fig. 2, and the method may include the following steps:
step S201, carrying out horizontal clustering on point clouds to be processed to obtain a plurality of horizontal clusters;
in the embodiment of the application, the point cloud to be processed is point cloud data obtained by scanning a target tree by scanning equipment, and the scanning equipment can be a ground laser radar, a backpack or a vehicle-mounted laser radar. When single tree segmentation is carried out on huge point cloud data, trunk seed points need to be extracted firstly to represent main bodies of single trees, the point cloud data are often gathered, horizontal clustering can be carried out, and a plurality of horizontal clusters are obtained so as to find the trunk seed points. The point cloud to be processed can be subjected to voxel downsampling, the point cloud is represented by a voxel gravity center point, and the data processing efficiency can be effectively improved.
Step S202, extracting trunk seed points of the target trees from the plurality of horizontal clusters;
in the embodiment of the present application, a representative point may be selected from the horizontal clusters obtained by clustering as a trunk seed point of a single tree, for example, the representative point may be represented by calculating a center of gravity point of the horizontal cluster.
Step S203, constructing a path map in the point cloud to be processed according to the tree trunk seed points;
in the embodiment of the application, after the trunk of the tree, namely the trunk seed point, is obtained, the trunk seed point can be used as an alternative point, and a path diagram for dividing the trunk seed point to which each point cloud belongs is constructed in the point cloud to be processed, namely the original point cloud, so that the shortest path growth of the tree is carried out.
Step S204, traversing and inquiring the shortest path point from the trunk seed point in the path map;
step S205, merging the current shortest path point to the single tree point set where the trunk seed point is located when the distance from the current shortest path point to the reference shortest path point adjacent to the current shortest path point is less than or equal to the distance threshold.
In the embodiment of the present application, the single-tree point set represents a set of points on a single tree corresponding to the tree trunk seed point, that is, points belonging to the same tree are classified into a single-tree point set. The distance threshold may be the maximum distance allowed for two adjacent points in the point cloud that make up the same tree. And (3) if the point is closest to a certain point on any tree in all the trees, the point is considered to be the same tree with the tree, the same ID is marked, and the point is divided into a single wood point set where the trunk seed point closest to the point is located. Points in each tree mark the total path and path distances from other points.
In the embodiment of the application, after the current shortest path point is classified as a single tree point set, the point is inserted into the trunk seed point and the path graph is updated to be used as a reference shortest path point when the next shortest path point is queried.
Optionally, an embodiment of the present application provides a method for preprocessing point cloud data before performing horizontal clustering on a point cloud to be processed to obtain a plurality of horizontal clusters, as shown in fig. 3, including the following steps:
step S301, denoising the point cloud to be processed to obtain a denoising point cloud;
step S302, classifying the noise reduction point cloud to obtain ground points and tree points;
step S303, taking the relative height between the ground point and the tree point as the height of the canopy of the tree point.
In the embodiment of the application, the point cloud to be processed includes all objects in the scanning area, even noise point data, and after the original point cloud data is obtained, the point cloud data needs to be denoised and classified, and the point cloud data can be divided into ground points and tree points, the ground points represent that the actual object corresponding to the point cloud data is the ground, the tree points represent that the actual object corresponding to the point cloud data is a tree, and the point cloud data after classification only changes the category attribute. The relative height between the ground point and the tree point is used as the canopy height of the tree point, namely the point cloud data after classification is normalized, and the tree point Z value can be subtracted from the ground point Z value to obtain the canopy height value of the tree point, so that the influence of topographic relief on subsequent single tree segmentation is reduced.
It should be noted that the normalization may be performed by using ground point normalization, or may be performed by using a Digital Elevation Model (DEM).
Optionally, performing horizontal clustering on the point cloud to be processed to obtain a plurality of horizontal clusters may include the following steps:
step 1, horizontally cutting the tree points at the height of the canopy according to a preset height threshold value to obtain a plurality of tree points in a horizontal layer;
and 2, performing horizontal Euclidean clustering on the tree points of each horizontal layer to obtain a plurality of horizontal clusters.
In the embodiment of the application, only the height of the canopy of the tree point is considered when the tree point is horizontally sliced, so that the influence caused by topographic relief is reduced, the more the number of slices is, the more the error rate of identifying the tree can be reduced, namely, the more the number of slices is, the smaller the influence of other short shrubs or other ground objects is. As a preferable solution of the embodiment of the present application, the number of the slice layers should not be less than 5, and the threshold height of the slice layer should not be less than 3 times the average dot pitch.
In the embodiment of the application, horizontal clustering is performed on the tree points of each horizontal layer, specifically, horizontal Euclidean clustering is performed on the tree points of each horizontal layer, that is, Euclidean clustering is performed according to the two-dimensional XY direction, so that a plurality of horizontal clusters are obtained.
Optionally, an optional trunk seed point extraction manner is provided in the embodiment of the present application, and as shown in fig. 4, the step S202 of extracting the trunk seed point of the target tree from the plurality of horizontal clusters may include:
step S401, calculating the gravity center point of each horizontal cluster, and adding a non-tree trunk seed point mark for each gravity center point;
step S402, a gravity center point in a layer of horizontal layer closest to the ground point is used as a first alternative trunk seed point, and a trunk seed point mark is added to the first alternative trunk seed point;
step S403, calculating first directions of N gravity points and a first alternative trunk seed point in a target horizontal layer, wherein the target horizontal layer is other horizontal layers except the horizontal layer where the first alternative trunk seed point is located;
step S404, calculating a second direction between any two gravity points in the N gravity points of the target horizontal layer;
step S405, under the condition that the included angle between the first direction and the ground is larger than an angle threshold value, taking a group of gravity points with the minimum included angle formed by the first direction and the second direction as second alternative trunk seed points, and adding trunk seed point marks for the second alternative trunk seed points;
and step S406, taking the second alternative trunk seed point as a final trunk seed point under the condition that the second alternative trunk seed point meets the preset condition.
In the embodiment of the application, when the trunk seed points of the target trees are extracted from the plurality of horizontal clusters, the gravity center points of the horizontal clusters in each horizontal layer are calculated at first, and the non-trunk seed point marks are added to the gravity center points at the moment. A gravity point with a non-stem seed point identification indicates that the gravity point is not a stem seed point. Calculating the center of gravity point may be summing the X, Y and Z values of the spatial three-dimensional components of each point in a horizontal cluster, respectively, divided by the total number of points.
In the embodiment of the application, a horizontal layer closest to the ground is used as a 0 layer, point cloud data in the layer is used as data of the 0 layer, and the like. And taking the gravity center point of each horizontal cluster in the data of the layer 0 as a candidate tree trunk point, namely a first candidate tree trunk seed point, and changing the non-tree trunk seed point mark added in the previous step of the gravity center point into a tree trunk seed point mark to show that the gravity center point can be taken as a tree trunk seed point. Because the closer the tree is to the ground, the more likely it is a trunk, according to common sense of life.
In the embodiment of the application, each gravity center point (namely, the first alternative trunk seed point of the 0 layer) is searched upwards layer by layer from the 0 layer, N points are calculated, a first direction a between the gravity center point and the searched N points and a second direction Bi of any two points of the N points are calculated, a group of points with the smallest included angle between the first direction a and the second direction Bi is obtained and the included angle between the first direction a and the ground larger than a certain angle threshold value is used as the second alternative trunk point, the original non-trunk seed point identification is changed into the trunk seed point identification, and each point of the N points is calculated in sequence. According to the growth mode of the trees, the trunk has direction consistency, so that when the included angle between the first direction A and the second direction Bi is the smallest, the trunk is the same tree and has the largest probability, meanwhile, the tree grows to form a certain included angle with the ground, under the normal condition, the vertical growth is the included angle of 90 degrees, the threshold value of the angle, namely the threshold value of the included angle between the tree and the ground, can be set to be 30 degrees, and errors are reduced when the tree is identified.
In the embodiment of the application, after the preliminary screening is completed, all the second alternative trunk seed points are screened again, and the gravity center points which are larger than the K points and have the height larger than a certain threshold value are reserved as the final trunk seed points.
Optionally, before traversing the shortest path point from the query to the trunk seed point in the path graph, creating a barycentric point set as follows:
step 1, carrying out European-style clustering of three-dimensional space on tree points to obtain a plurality of spatial clusters;
step 2, selecting a plurality of target clusters from the plurality of spatial clusters, wherein the target clusters are spatial clusters of which the clustering points are greater than a screening threshold value;
and 3, calculating a gravity center point set consisting of gravity center points of a plurality of target clusters, wherein the gravity center points in the gravity center point set are sorted from large to small according to the number of cluster points in the target cluster where the gravity center points are located.
In the embodiment of the application, spatial three-dimensional Euclidean clustering can be performed on point cloud data to accelerate data processing, specifically, three-dimensional Euclidean clustering is performed on tree points to obtain a plurality of spatial clusters, the spatial clusters with the clustering points larger than a screening threshold value are screened out, and therefore shortest path calculation is not performed on the spatial clusters with the clustering points smaller in the subsequent steps. Alternatively, the screening threshold may be 500 points.
In the embodiment of the application, after the spatial clusters with the clustering points larger than the screening threshold are screened, the gravity center points of the spatial clusters are calculated, and the gravity center points form a gravity center point set, wherein the ranking mode of the gravity center points in the gravity center point set is ranked from large to small according to the number of the clustering points in the spatial clusters where the gravity center points are located, so that most of the clustering points are preferentially solved when the shortest path point is queried, and the efficiency is higher.
Optionally, in a case that a distance from the current shortest-path point queried in the path graph to the reference shortest-path point adjacent to the current shortest-path point is greater than a distance threshold, the method further includes re-querying the shortest-path point as follows:
step 1, inquiring a nearest gravity point closest to a trunk seed point in a gravity point set;
and 2, inquiring the shortest path point closest to the trunk seed point in the target cluster corresponding to the closest gravity center point, and merging the shortest path point to the single tree point set where the trunk seed point is located.
In the embodiment of the application, when the distance from the inquired current shortest path point to the reference shortest path point adjacent to the inquired current shortest path point is greater than the maximum distance allowed by two adjacent points in the point cloud forming the same tree, the nearest gravity point closest to the trunk seed point is inquired from the gravity point set, and the shortest path point closest to the trunk seed point is inquired again in the spatial cluster where the nearest gravity point is located.
Optionally, after merging the current shortest path point into the single tree point set where the trunk seed point is located, the method further includes:
step 1, performing voxelization downsampling on point cloud to be processed to obtain a voxel gravity center point;
and 2, constructing a k-d tree for the voxel gravity center point according to the single tree segmentation relation represented by the single tree point set, and restoring the single tree mark to the point cloud to be processed according to the nearest neighbor mode.
In the embodiment of the application, after all the downsampled points are subjected to tree growth, the points need to be restored into the original point cloud, and the original point cloud single tree mark can be restored by using a nearest neighbor query method.
According to the method, horizontal clustering is carried out on point clouds to be processed to obtain a plurality of horizontal clusters, wherein the point clouds to be processed are point cloud data obtained by scanning a target tree through scanning equipment; extracting trunk seed points of the target trees from the plurality of horizontal clusters; constructing a path diagram in the point cloud to be processed according to the tree trunk seed points; traversing and inquiring a shortest path point from the trunk seed point in the path graph; under the condition that the distance from the current shortest path point to the reference shortest path point adjacent to the current shortest path point is smaller than or equal to the distance threshold, the current shortest path point is merged to a single tree point set where the trunk seed point is located, wherein the single tree point set represents a point cloud single tree segmentation method of a set of points on single trees belonging to the trunk seed point, the influence of branch and trunk intersection among multiple trees on single tree segmentation is effectively reduced, the segmentation precision is improved, and the segmentation efficiency is accelerated.
According to another aspect of the embodiments of the present application, as shown in fig. 5, there is provided a point cloud single wood segmentation apparatus including: the clustering module 501 is configured to perform horizontal clustering on point clouds to be processed to obtain a plurality of horizontal clusters, where the point clouds to be processed are point cloud data obtained by scanning a target tree by a scanning device; an extraction module 502 for extracting trunk seed points of a target tree from a plurality of horizontal clusters; a building module 503, configured to build a path map in the point cloud to be processed according to the tree trunk seed point; a query module 504, configured to traverse a shortest path point from the trunk seed point in the path map; and an allocating module 505, configured to merge the current shortest path point into a single tree point set where the trunk seed point is located when a distance from the current shortest path point to a reference shortest path point adjacent to the current shortest path point is less than or equal to a distance threshold, where the single tree point set represents a set of points on single trees belonging to the trunk seed point.
It should be noted that the clustering module 501 in this embodiment may be configured to execute step S201 in this embodiment, the extracting module 502 in this embodiment may be configured to execute step S202 in this embodiment, the constructing module 503 in this embodiment may be configured to execute step S203 in this embodiment, the querying module 504 in this embodiment may be configured to execute step S204 in this embodiment, and the allocating module 505 in this embodiment may be configured to execute step S205 in this embodiment.
According to the point cloud single tree segmentation method (namely, the rapid point cloud single tree segmentation method based on the shortest path and the Euclidean clustering algorithm), the obtained point cloud to be segmented is subjected to ground point classification and then is normalized; performing voxel downsampling on the point cloud data, and replacing an original point cloud with a voxel gravity center point; extracting data from the ground to a preset height range from the normalized data, and extracting trunk point cloud as a tree growth seed point; carrying out Euclidean clustering on the data according to a set threshold value, and constructing a path diagram on the point cloud data; and taking the trunk seed point cloud as an alternative point, traversing and inquiring the shortest path point from the seed point, and circularly traversing the alternative point until all the points are merged into the target point set.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules described above as a part of the apparatus may operate in a hardware environment as shown in fig. 1, and may be implemented by software or hardware.
Optionally, the point cloud single wood segmentation apparatus further includes: the denoising module is used for denoising the point cloud to be processed to obtain a denoising point cloud; the classification module is used for classifying the noise reduction point cloud to obtain ground points and tree points; and the normalization module is used for taking the relative height between the ground point and the tree point as the height of the canopy of the tree point.
Optionally, the point cloud single wood segmentation apparatus further includes: the horizontal layer cutting module is used for horizontally cutting the tree points at the height of the canopy according to a preset height threshold value to obtain a plurality of horizontal layers of tree points; and the horizontal Euclidean clustering module is used for performing horizontal Euclidean clustering on the tree points of each horizontal layer to obtain a plurality of horizontal clusters.
Optionally, the point cloud single wood segmentation apparatus further includes: the gravity center point calculation module is used for calculating the gravity center point of each horizontal cluster and adding non-tree-trunk seed point identification to each gravity center point; the first alternative trunk seed point selection module is used for taking a gravity center point in a layer of horizontal layer closest to the ground point as a first alternative trunk seed point and adding a trunk seed point mark for the first alternative trunk seed point; the first direction calculation module is used for calculating the first direction of the N gravity points and the first alternative trunk seed points in the target horizontal layer, wherein the target horizontal layer is other horizontal layers except the horizontal layer where the first alternative trunk seed points are located; the second direction calculation module is used for calculating a second direction between any two gravity points in the N gravity points of the target horizontal layer; the second alternative trunk seed point selection module is used for taking a group of gravity points with the minimum included angle formed by the first direction and the second direction as second alternative trunk seed points and adding trunk seed point marks for the second alternative trunk seed points under the condition that the included angle between the first direction and the ground is larger than an angle threshold value; and the confirming module is used for taking the second alternative trunk seed point as the final trunk seed point under the condition that the second alternative trunk seed point meets the preset condition.
Optionally, the point cloud single wood segmentation apparatus further includes: the spatial Euclidean clustering module is used for carrying out Euclidean clustering on tree points in a three-dimensional space to obtain a plurality of spatial clusters; the screening module is used for selecting a plurality of target clusters from the plurality of spatial clusters, wherein the target clusters are spatial clusters of which the clustering points are greater than a screening threshold value; and the gravity center point set calculation module is used for calculating a gravity center point set consisting of gravity center points of a plurality of target clusters, wherein the gravity center points in the gravity center point set are sorted from large to small according to the number of the cluster points in the target cluster.
Optionally, the point cloud single wood segmentation apparatus further includes: the nearest gravity point searching module is used for searching nearest gravity points nearest to the trunk seed points in the gravity point set; and the shortest path point searching module is used for searching the shortest path point closest to the trunk seed point in the target cluster corresponding to the closest gravity center point, and merging the shortest path point into the single tree point set where the trunk seed point is located.
Optionally, the point cloud single wood segmentation apparatus further includes: the voxel down-sampling module is used for carrying out voxel down-sampling on the point cloud to be processed to obtain a voxel gravity center point; and the neighborhood query module is used for constructing k-dtree for the voxel gravity center point according to the single tree segmentation relation represented by the single tree point set so as to restore the single tree mark to the point cloud to be processed in a nearest neighbor mode.
The point cloud single tree segmentation method provided by the embodiment of the invention (i.e. the rapid point cloud single tree segmentation method based on the shortest path and the Euclidean clustering algorithm) also has the following technical advantages, for example: 1. the method solves the problems of low single tree segmentation precision and poor effect (namely, the method (shortest path and European clustering algorithm) can be used for effectively solving the problem that the branches and the trunks among trees can not be distinguished, and particularly, the method has more obvious effect compared with other algorithms in a dense forest area.2, the single tree segmentation efficiency is obviously improved (namely, the shortest path can be effectively solved, but the space complexity and the time complexity of a point-by-point calculation mode are higher under the principle of the shortest path algorithm) through early voxel resampling and European clustering.
There is also provided, in accordance with yet another aspect of the embodiments of the present application, a computer device, including a memory and a processor, the memory having stored therein a computer program executable on the processor, the processor implementing the steps when executing the computer program.
The memory and the processor in the computer device communicate with each other through a communication bus and a communication interface. The communication bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc.
The Memory may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
A computer-readable medium is also provided in accordance with yet another aspect of an embodiment of the present application.
Optionally, in an embodiment of the present application, a computer readable medium is configured to store program code for the processor to perform the following steps:
step S201, carrying out horizontal clustering on point clouds to be processed to obtain a plurality of horizontal clusters;
step S202, extracting trunk seed points of the target trees from the plurality of horizontal clusters;
step S203, constructing a path map in the point cloud to be processed according to the tree trunk seed points;
step S204, traversing and inquiring the shortest path point from the trunk seed point in the path map;
step S205, merging the current shortest path point to the single tree point set where the trunk seed point is located when the distance from the current shortest path point to the reference shortest path point adjacent to the current shortest path point is less than or equal to the distance threshold.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
When the embodiments of the present application are specifically implemented, reference may be made to the above embodiments, and corresponding technical effects are achieved.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the Processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk. It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A point cloud single wood segmentation method is characterized by comprising the following steps:
performing horizontal clustering on point clouds to be processed to obtain a plurality of horizontal clusters, wherein the point clouds to be processed are point cloud data obtained by scanning a target tree by scanning equipment;
extracting trunk seed points of the target tree from a plurality of the horizontal clusters;
constructing a path diagram in the point cloud to be processed according to the trunk seed points;
traversing the shortest path point from the trunk seed point to the query in the path graph;
merging the current shortest path point to a single tree point set where the trunk seed point is located when the distance from the current shortest path point to a reference shortest path point adjacent to the current shortest path point is less than or equal to a distance threshold, wherein the single tree point set represents a set of points on single trees belonging to the trunk seed point.
2. The method of claim 1, wherein before performing horizontal clustering on the point cloud to be processed to obtain a plurality of horizontal clusters, the method further comprises preprocessing the point cloud to be processed as follows:
denoising the point cloud to be processed to obtain a denoising point cloud;
classifying the noise reduction point cloud to obtain ground points and tree points;
and taking the relative height of the ground point from the tree point as the canopy height of the tree point.
3. The method of claim 2, wherein performing horizontal clustering on the point cloud to be processed to obtain a plurality of horizontal clusters comprises:
horizontally cutting the tree points at the height of the canopy according to a preset height threshold value to obtain a plurality of horizontal layers of tree points;
and carrying out horizontal Euclidean clustering on the tree points of each horizontal layer to obtain a plurality of horizontal clusters.
4. The method of claim 3, wherein extracting the stem seed point of the target tree from the plurality of horizontal clusters comprises:
calculating the gravity center point of each horizontal cluster, and adding a non-tree-trunk seed point identifier for each gravity center point;
taking the gravity center point in a layer of horizontal layer closest to the ground point as a first alternative trunk seed point, and adding a trunk seed point mark for the first alternative trunk seed point;
calculating a first direction of the N gravity center points and the first alternative trunk seed point in a target horizontal layer, wherein the target horizontal layer is other horizontal layers except the horizontal layer where the first alternative trunk seed point is located;
calculating a second direction between any two of the gravity points in the N gravity points of the target horizontal layer;
under the condition that the included angle between the first direction and the ground is larger than an angle threshold value, taking a group of gravity points with the smallest included angle formed by the first direction and the second direction as second alternative trunk seed points, and adding trunk seed point marks for the second alternative trunk seed points;
and under the condition that the second alternative trunk seed point meets the preset condition, taking the second alternative trunk seed point as the final trunk seed point.
5. The method of claim 2, further comprising, prior to traversing a shortest path point of a query from the stem seed point in the path graph, creating a set of barycentric points as follows:
carrying out European-style clustering of three-dimensional space on the tree points to obtain a plurality of spatial clusters;
selecting a plurality of target clusters from the plurality of spatial clusters, wherein the target clusters are spatial clusters of which the clustering points are greater than a screening threshold value;
and calculating the gravity center point set consisting of the gravity center points of the plurality of target clusters, wherein the gravity center points in the gravity center point set are sorted from large to small according to the number of the cluster points in the target cluster.
6. The method of claim 5, wherein if the distance from the current shortest path point queried in the path map to the reference shortest path point adjacent to the current shortest path point is greater than the distance threshold, the method further comprises re-querying the shortest path point as follows:
querying a closest gravity point closest to the trunk seed point in the gravity point set;
and inquiring the shortest path point closest to the trunk seed point in the target cluster corresponding to the closest gravity center point, and merging the shortest path point to the single tree point set where the trunk seed point is located.
7. The method of any one of claims 1 to 6, wherein merging the current shortest path point after the set of single wood points where the trunk seed point is located, the method further comprises:
performing voxelization downsampling on the point cloud to be processed to obtain a voxel gravity center point;
and constructing a k-d tree for the voxel gravity center point according to the single tree segmentation relation represented by the single tree point set so as to restore the single tree mark to the point cloud to be processed in a nearest neighbor mode.
8. A point cloud single wood segmentation device is characterized by comprising:
the system comprises a clustering module, a processing module and a processing module, wherein the clustering module is used for horizontally clustering point clouds to be processed to obtain a plurality of horizontal clusters, and the point clouds to be processed are point cloud data obtained by scanning a target tree by scanning equipment;
an extraction module for extracting a trunk seed point of the target tree from the plurality of horizontal clusters;
the construction module is used for constructing a path map in the point cloud to be processed according to the trunk seed points;
the query module is used for traversing and querying the shortest path point which is away from the trunk seed point in the path graph;
and the distribution module is used for merging the current shortest path point into the single tree point set where the trunk seed point is located under the condition that the distance from the current shortest path point to the reference shortest path point adjacent to the current shortest path point is smaller than or equal to a distance threshold, wherein the single tree point set represents a set of points on single trees belonging to the trunk seed point.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer readable medium, characterized in that said program code causes said processor to execute the method of any of the claims 1 to 7.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112308839A (en) * | 2020-10-31 | 2021-02-02 | 云南师范大学 | Single-tree segmentation method and device for natural forest, computer equipment and storage medium |
| CN113095571A (en) * | 2021-04-14 | 2021-07-09 | 河北农业大学 | Biomass model prediction method and system based on tree metabolic rate theory |
| CN114387288A (en) * | 2022-01-17 | 2022-04-22 | 滁州学院 | Single standing tree three-dimensional information extraction method based on vehicle-mounted laser radar point cloud data |
| CN115953607A (en) * | 2023-01-04 | 2023-04-11 | 北京数字绿土科技股份有限公司 | Trunk diameter at breast height extraction method and system using point cloud data |
| CN116310849A (en) * | 2023-05-22 | 2023-06-23 | 深圳大学 | Tree point cloud monomerization extraction method based on three-dimensional morphological characteristics |
| CN117710601A (en) * | 2023-12-27 | 2024-03-15 | 南京林业大学 | Single wood skeleton extraction method and system based on laser point cloud and image information |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114510771B (en) * | 2022-04-07 | 2022-07-08 | 深圳小库科技有限公司 | Method, device and equipment for generating mold of building curved surface and computer readable medium |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101922914A (en) * | 2010-08-27 | 2010-12-22 | 中国林业科学研究院资源信息研究所 | Crown information extraction method and system based on high spatial resolution remote sense image |
| US20160027208A1 (en) * | 2014-07-25 | 2016-01-28 | Kabushiki Kaisha Toshiba | Image analysis method |
| CN105894044A (en) * | 2016-04-29 | 2016-08-24 | 武汉大学 | Single-plant tree point cloud automatic extraction method based on vehicle-mounted laser scanning data |
| CN109712229A (en) * | 2018-11-26 | 2019-05-03 | 漳州通正勘测设计院有限公司 | A kind of isolated tree wooden frame point extracting method, device, equipment and storage medium |
| CN110992473A (en) * | 2019-12-13 | 2020-04-10 | 武汉大学 | Tree branch modeling method and system based on vehicle-mounted laser scanning point cloud |
-
2020
- 2020-05-19 CN CN202010422711.3A patent/CN111598915B/en active Active
- 2020-05-28 WO PCT/CN2020/092868 patent/WO2021232467A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101922914A (en) * | 2010-08-27 | 2010-12-22 | 中国林业科学研究院资源信息研究所 | Crown information extraction method and system based on high spatial resolution remote sense image |
| US20160027208A1 (en) * | 2014-07-25 | 2016-01-28 | Kabushiki Kaisha Toshiba | Image analysis method |
| CN105894044A (en) * | 2016-04-29 | 2016-08-24 | 武汉大学 | Single-plant tree point cloud automatic extraction method based on vehicle-mounted laser scanning data |
| CN109712229A (en) * | 2018-11-26 | 2019-05-03 | 漳州通正勘测设计院有限公司 | A kind of isolated tree wooden frame point extracting method, device, equipment and storage medium |
| CN110992473A (en) * | 2019-12-13 | 2020-04-10 | 武汉大学 | Tree branch modeling method and system based on vehicle-mounted laser scanning point cloud |
Non-Patent Citations (2)
| Title |
|---|
| 瞿帅 等: "机载激光雷达森林资源调查***的设计与试验", 《西北林学院学报》 * |
| 黄笑: "风力载荷下树木安全性评估", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112308839A (en) * | 2020-10-31 | 2021-02-02 | 云南师范大学 | Single-tree segmentation method and device for natural forest, computer equipment and storage medium |
| CN112308839B (en) * | 2020-10-31 | 2022-08-02 | 云南师范大学 | Single-tree segmentation method and device for natural forest, computer equipment and storage medium |
| CN113095571A (en) * | 2021-04-14 | 2021-07-09 | 河北农业大学 | Biomass model prediction method and system based on tree metabolic rate theory |
| CN113095571B (en) * | 2021-04-14 | 2022-08-09 | 河北农业大学 | Biomass model prediction method and system based on tree metabolic rate theory |
| CN114387288A (en) * | 2022-01-17 | 2022-04-22 | 滁州学院 | Single standing tree three-dimensional information extraction method based on vehicle-mounted laser radar point cloud data |
| CN115953607A (en) * | 2023-01-04 | 2023-04-11 | 北京数字绿土科技股份有限公司 | Trunk diameter at breast height extraction method and system using point cloud data |
| CN115953607B (en) * | 2023-01-04 | 2024-02-13 | 北京数字绿土科技股份有限公司 | Trunk breast diameter extraction method and system using point cloud data |
| CN116310849A (en) * | 2023-05-22 | 2023-06-23 | 深圳大学 | Tree point cloud monomerization extraction method based on three-dimensional morphological characteristics |
| CN116310849B (en) * | 2023-05-22 | 2023-09-19 | 深圳大学 | Tree point cloud monomerization extraction method based on three-dimensional morphological characteristics |
| CN117710601A (en) * | 2023-12-27 | 2024-03-15 | 南京林业大学 | Single wood skeleton extraction method and system based on laser point cloud and image information |
| CN117710601B (en) * | 2023-12-27 | 2024-05-24 | 南京林业大学 | Single wood skeleton extraction method and system based on laser point cloud and image information |
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