CN113178004A - Method for displaying mass point cloud based on hyper-kernel octree - Google Patents
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Abstract
The invention discloses a method for displaying mass point clouds based on a supercore octree, which belongs to the technical field of information and comprises the following specific steps: (1) acquiring a mass of point clouds; (2) partitioning a mass point cloud space; (3) creating a space index of mass point clouds; (4) merging and displaying octree; according to the invention, the massive point cloud data is rapidly divided into the small blocks by utilizing a layering counting sorting method, then the local octree is generated for each small block, and the sub-sampling is carried out by adopting a layering approximate blue noise sub-sampling algorithm, so that the efficiency of loading the massive point cloud data by a program is improved, the rapid display is carried out, the time required by data loading is reduced, the user experience is improved, and the effect of rapidly loading and displaying the massive point cloud data is achieved.
Description
Technical Field
The invention relates to the technical field of information, in particular to a method for displaying mass point clouds based on a hyper-kernel octree.
Background
Through retrieval, the chinese patent No. CN107943961A discloses a massive point cloud rendering method based on the Web, which allocates point cloud data through a quadtree data structure and renders the point cloud data onto a Web browser by using a Potree, but cannot effectively cope with the processing flow of the current massive point cloud data and cannot rapidly display the massive point cloud data in real time; the laser radar scanning technology is a novel three-dimensional data acquisition technology, and mass point cloud data can be rapidly acquired by using laser radar scanners carried on different platforms such as tripods, automobiles, airplanes and satellites; the point cloud data contains rich information such as longitude and latitude coordinates, strength, multiple echoes, colors and the like of each point, and has related application in the fields of surveying and mapping, forestry, agriculture, digital cities and the like; currently, a common laser radar scanner device, such as Riegl, Faro, Leica and the like, can generate thousands of points per second, the number of data points acquired by each scanning can reach hundreds of thousands or millions, and the data volume reaches dozens to hundreds of G; the huge data volume brings troubles to the storage, processing and display of data; therefore, it becomes more important to invent a display method of mass point cloud based on hyper-kernel octree;
most of the existing point cloud display methods utilize structures such as a quadtree or a K-D tree to divide and organize a point cloud data set into a hierarchical structure, then a multi-resolution data structure under error control is generated for each piece of divided data, and proper hierarchical data is extracted according to requirements on detail hierarchy for rendering during visualization, although the method can visualize point cloud data, the method cannot effectively cope with the processing flow of the existing massive point cloud data, and cannot rapidly display the massive point cloud data in real time; therefore, a method for displaying mass point clouds based on a hyper-kernel octree is provided.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a supercore octree-based mass point cloud display method.
In order to achieve the purpose, the invention adopts the following technical scheme:
a mass point cloud display method based on a hyper-kernel octree specifically comprises the following steps:
(1) acquiring mass point cloud: acquiring a mass point cloud data file, and analyzing the mass point cloud data file to obtain mass point cloud data;
(2) partitioning a mass point cloud space: calculating bounding boxes of mass point cloud data, and partitioning the mass point cloud data by using a counting and sorting algorithm to obtain a plurality of partitioned point cloud bounding boxes;
(3) creating a mass point cloud space index: constructing a local octree for each partitioned point cloud bounding box, and simultaneously establishing a spatial index for the local octree;
(4) octree merge display: and combining a plurality of local octrees to form a global octree, and loading and displaying the global octree through a disk.
Further, the process of establishing the local octree specifically includes:
s1: firstly, irregularly dividing mass point cloud data by adopting a counting and sorting method to form a plurality of cube bounding boxes, and taking the cube bounding boxes of all point clouds as root nodes of an octree;
s2: then, performing second-class division on the cubic bounding box in a direction parallel to the X axis, the Y axis and the Z axis 3 respectively; obtaining 8 subcubes, namely child nodes, corresponding to the root nodes;
s3: then, each sub-node is subjected to recursive segmentation according to the same rule to form leaf nodes, and when the length of the edge of the cube is smaller than a preset value, the node is not subjected to segmentation;
s4: taking a plurality of cube bounding boxes as three-dimensional grids, and calculating the number of all point clouds in each grid unit through a grid counter;
s5: after calculating the number of all point clouds in each grid unit, recursively combining small grid units which are sparse enough into larger grid units, wherein the combination is realized in a mode of creating a pyramid, and if the number of points in each grid unit is smaller than a set threshold value, dividing the points into the low level of the pyramid; if the position of the point is larger than the set threshold value, adding the positions and the levels of all the points larger than zero into the list;
s6: creating a lookup table, creating a lookup table with the same size as the grid unit, and constructing a pointer from the grid unit to the corresponding cubic bounding box;
s7: finally, iterating all point clouds again, projecting the point clouds into a grid unit cell, simultaneously accessing a lookup table, retrieving a target cube bounding box by using a pointer, and writing the bounding box into a file;
each octree holds a subsample of one complete point cloud, with the root node containing a coarse low-density subsample of the entire mass point cloud data, and with each level, the resolution is doubled.
Further, the spatial index creation process is specifically as follows:
SS 1: dividing the point cloud in each cube bounding box into leaf nodes with the appointed maximum point number by using a hierarchical counting sorting method;
SS 2: the coarser level of detail is then populated by the bottom-up recursive sub-nodes.
Further, the sub-sampling is layered according to the sampling levels, and each layer of point cloud surrounds a box frame to perform approximate sampling, wherein the process is as follows:
SSS 1: firstly, inputting mass point cloud data, and carrying out internal sequencing according to the distance from the mass point cloud data to the center of a cubic bounding box;
SSS 2: then, for each input point cloud, checking the distance to the previously accepted point, in an external order, by cycling through a list of accepted points, which is an implicit inside-outside ordering, starting from the end;
SSS 3: if the difference between the center and the candidate point and the difference between the center and the acceptance point are greater than the minimum distance, the current candidate point is safely accepted.
Further, the plurality of block point cloud bounding boxes represent local octrees of their respective locations, each time a block is fully processed, its root node is linked to a global octree.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the supercore octree-based mass point cloud display method, the mass point cloud data are quickly segmented by adopting a layering counting and sorting method, so that a large amount of point cloud data can be sequentially and reasonably stored in a storage space, and the stability and the efficiency of computer operation are greatly improved; meanwhile, the storage space can be effectively and reasonably used, and the probability of computer rendering breakdown is reduced;
2. according to the method for displaying the mass point cloud based on the hyper-kernel octree, the local octree is generated for each small block, and sub-sampling is performed through a layered approximate blue noise sub-sampling algorithm, so that the efficiency of loading mass point cloud data by a program is improved, the mass point cloud data is displayed quickly, the time required by data loading is reduced, the user experience is improved, and the effect of rapidly loading and displaying the mass point cloud data is achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is an overall flowchart of a supercore octree-based mass point cloud display method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Referring to fig. 1, a method for displaying a supercore octree-based mass point cloud, the method specifically comprises the following steps:
(1) acquiring mass point cloud: acquiring a mass point cloud data file, and analyzing the mass point cloud data file to obtain mass point cloud data;
(2) partitioning a mass point cloud space: calculating bounding boxes of mass point cloud data, and partitioning the mass point cloud data by using a counting and sorting algorithm to obtain a plurality of partitioned point cloud bounding boxes;
(3) creating a mass point cloud space index: constructing a local octree for each partitioned point cloud bounding box, and simultaneously establishing a spatial index for the local octree;
(4) octree merge display: and combining a plurality of local octrees to form a global octree, and loading and displaying the global octree through a disk.
The specific scheme of the embodiment is as follows: the process of establishing the local octree is as follows:
s1: firstly, irregularly dividing mass point cloud data by adopting a counting and sorting method to form a plurality of cube bounding boxes, and taking the cube bounding boxes of all point clouds as root nodes of an octree;
s2: then, performing second-class division on the cubic bounding box in a direction parallel to the X axis, the Y axis and the Z axis 3 respectively; obtaining 8 subcubes, namely child nodes, corresponding to the root nodes;
s3: then, each sub-node is subjected to recursive segmentation according to the same rule to form leaf nodes, and when the length of the edge of the cube is smaller than a preset value, the node is not subjected to segmentation;
s4: taking a plurality of cube bounding boxes as three-dimensional grids, and calculating the number of all point clouds in each grid unit through a grid counter;
s5: after calculating the number of all point clouds in each grid unit, recursively combining small grid units which are sparse enough into larger grid units, wherein the combination is realized in a mode of creating a pyramid, and if the number of points in each grid unit is smaller than a set threshold value, dividing the points into the low level of the pyramid; if the position of the point is larger than the set threshold value, adding the positions and the levels of all the points larger than zero into the list;
s6: creating a lookup table, creating a lookup table with the same size as the grid unit, and constructing a pointer from the grid unit to the corresponding cubic bounding box;
s7: finally, iterating all point clouds again, projecting the point clouds into a grid unit cell, simultaneously accessing a lookup table, retrieving a target cube bounding box by using a pointer, and writing the bounding box into a file;
each octree holds a subsample of one complete point cloud, with the root node containing a coarse low-density subsample of the entire mass point cloud data, and with each level, the resolution is doubled.
The specific scheme of the embodiment is as follows: the spatial index creation process is specifically as follows:
SS 1: dividing the point cloud in each cube bounding box into leaf nodes with the appointed maximum point number by using a hierarchical counting sorting method;
SS 2: the coarser level of detail is then populated by the bottom-up recursive sub-nodes.
The sub-sampling is layered according to the sampling level, and each layer of point cloud surrounds a box frame to perform approximate sampling, and the process is as follows:
SSS 1: firstly, inputting mass point cloud data, and carrying out internal sequencing according to the distance from the mass point cloud data to the center of a cubic bounding box;
SSS 2: then, for each input point cloud, checking the distance to the previously accepted point, in an external order, by cycling through a list of accepted points, which is an implicit inside-outside ordering, starting from the end;
SSS 3: if the difference between the center and the candidate point and the difference between the center and the acceptance point are greater than the minimum distance, the current candidate point is safely accepted.
Multiple block point cloud bounding boxes represent local octrees of their respective locations, with their root nodes linked to a global octree each time a block is fully processed.
The working principle and the using process of the invention are as follows: when the method for displaying the mass point cloud based on the hyper-kernel octree is used, firstly, the mass point cloud is obtained: acquiring a mass point cloud data file, and analyzing the mass point cloud data file to obtain mass point cloud data; then, partitioning a mass point cloud space: calculating bounding boxes of mass point cloud data, and partitioning the mass point cloud data by using a counting and sorting algorithm to obtain a plurality of partitioned point cloud bounding boxes; then creating a mass point cloud space index: constructing a local octree for each partitioned point cloud bounding box, wherein the specific process is as follows: the method comprises the following steps that firstly, mass point cloud data are subjected to irregular segmentation by adopting a counting sorting method to form a plurality of cube bounding boxes, and the cube bounding boxes of all point clouds are used as root nodes of an octree; secondly, performing second-order segmentation on the cubic bounding box in a direction parallel to the X axis, the Y axis and the Z axis 3 respectively; obtaining 8 subcubes, namely child nodes, corresponding to the root nodes; thirdly, performing recursive segmentation on each sub-node according to the same rule to form leaf nodes, and when the length of the edge of the cube is smaller than a preset value, the node is not segmented; fourthly, taking a plurality of cube bounding boxes as three-dimensional grids, and calculating the number of all point clouds in each grid unit through a grid counter; step five, after calculating the number of all point clouds in each grid unit, recursively combining small grid units which are sparse enough into larger grid units, wherein the combination is realized in a mode of creating a pyramid, and if the number of points in each grid unit is less than a set threshold value, dividing the grid units into the lower level of the pyramid; if the position of the point is larger than the set threshold value, adding the positions and the levels of all the points larger than zero into the list; sixthly, creating a lookup table with the same size as the grid unit, and constructing a pointer from the grid unit to the corresponding cubic bounding box; finally, iterating all point clouds again, projecting the point clouds into a grid unit cell, simultaneously accessing a lookup table, retrieving a target cube bounding box by using a pointer, and writing the bounding box into a file; then creating a spatial index for the local octree; and finally, merging and displaying the octree: combining a plurality of local octrees to form a global octree, and loading and displaying the global octree through a disk; according to the invention, the massive point cloud data is rapidly divided into the small blocks by utilizing a layering counting sorting method, then the local octree is generated for each small block, and the sub-sampling is carried out by adopting a layering approximate blue noise sub-sampling algorithm, so that the efficiency of loading the massive point cloud data by a program is improved, the rapid display is carried out, the time required by data loading is reduced, the user experience is improved, and the effect of rapidly loading and displaying the massive point cloud data is achieved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (5)
1. A mass point cloud display method based on a hyper-kernel octree is characterized by comprising the following specific steps:
(1) acquiring mass point cloud: acquiring a mass point cloud data file, and analyzing the mass point cloud data file to obtain mass point cloud data;
(2) partitioning a mass point cloud space: calculating bounding boxes of mass point cloud data, and partitioning the mass point cloud data by using a counting and sorting algorithm to obtain a plurality of partitioned point cloud bounding boxes;
(3) creating a mass point cloud space index: constructing a local octree for each partitioned point cloud bounding box, and simultaneously establishing a spatial index for the local octree;
(4) octree merge display: and combining a plurality of local octrees to form a global octree, and loading and displaying the global octree through a disk.
2. The supercore octree-based mass point cloud display method according to claim 1, wherein the process of establishing the local octree is specifically as follows:
s1: firstly, irregularly dividing mass point cloud data by adopting a counting and sorting method to form a plurality of cube bounding boxes, and taking the cube bounding boxes of all point clouds as root nodes of an octree;
s2: then, performing second-class division on the cubic bounding box in a direction parallel to the X axis, the Y axis and the Z axis 3 respectively; obtaining 8 subcubes, namely child nodes, corresponding to the root nodes;
s3: then, each sub-node is subjected to recursive segmentation according to the same rule to form leaf nodes, and when the length of the edge of the cube is smaller than a preset value, the node is not subjected to segmentation;
s4: taking a plurality of cube bounding boxes as three-dimensional grids, and calculating the number of all point clouds in each grid unit through a grid counter;
s5: after calculating the number of all point clouds in each grid unit, recursively combining small grid units which are sparse enough into larger grid units, wherein the combination is realized in a mode of creating a pyramid, and if the number of points in each grid unit is smaller than a set threshold value, dividing the points into the low level of the pyramid; if the position of the point is larger than the set threshold value, adding the positions and the levels of all the points larger than zero into the list;
s6: creating a lookup table, creating a lookup table with the same size as the grid unit, and constructing a pointer from the grid unit to the corresponding cubic bounding box;
s7: finally, iterating all point clouds again, projecting the point clouds into a grid unit cell, simultaneously accessing a lookup table, retrieving a target cube bounding box by using a pointer, and writing the bounding box into a file;
each octree holds a subsample of one complete point cloud, with the root node containing a coarse low-density subsample of the entire mass point cloud data, and with each level, the resolution is doubled.
3. The supercore octree mass point cloud display method according to claim 1, wherein the spatial index creation process is specifically as follows:
SS 1: dividing the point cloud in each cube bounding box into leaf nodes with the appointed maximum point number by using a hierarchical counting sorting method;
SS 2: the coarser level of detail is then populated by the bottom-up recursive sub-nodes.
4. The method for displaying the hyper-kernel octree-based massive point cloud according to claim 3, wherein the sub-sampling is layered according to sampling levels, and each layer of point cloud surrounds a box frame to perform approximate sampling, and the process is as follows:
SSS 1: firstly, inputting mass point cloud data, and carrying out internal sequencing according to the distance from the mass point cloud data to the center of a cubic bounding box;
SSS 2: then, for each input point cloud, checking the distance to the previously accepted point, in an external order, by cycling through a list of accepted points, which is an implicit inside-outside ordering, starting from the end;
SSS 3: if the difference between the center and the candidate point and the difference between the center and the acceptance point are greater than the minimum distance, the current candidate point is safely accepted.
5. The supercore octree-based mass point cloud display method of claim 1, wherein said plurality of block point cloud bounding boxes represent local octrees of their respective locations, and each time a block is completely processed, its root node is linked to a global octree.
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