Fast scene modeling method based on linear storage structure of point cloud 3D sparse matrix

Abstract

This paper discloses a rapid scene modeling method based on a linear storage structure for a three-dimensional sparse matrix of a point cloud. The method includes data preprocessing, building a linear indexing mechanism and scene detail hierarchy, and point cloud data storage and retrieval. This method eliminates the addressing costs of traditional spatial data structures, reduces spatial complexity, and enables efficient storage and rapid retrieval of large-scale point cloud data. The proposed rapid scene modeling method offers significant advantages in data processing efficiency and rendering performance, making it suitable for scenarios requiring efficient spatial data processing and real-time rendering, such as wide-area-scale terrain data acquisition and analysis, and has promising application prospects.

Description

Technical Field

[0001] The present invention belongs to the technical field of large-scale three-dimensional space point cloud processing, and in particular relates to a fast scene modeling method based on a point cloud three-dimensional sparse matrix linear storage structure. Background Art

[0002] Large-scale 3D terrain scenes are a crucial component of 3D virtual environments. Rapid and efficient acquisition of scene data provides the foundation for spatial analysis and deformation monitoring. This is particularly true for wide-area slopes, particularly those requiring large terrain datasets and complex and diverse surface features due to the vast area and complex geological structures. The large size of point cloud datasets inevitably negatively impacts subsequent processing, storage, transmission, and display. Geomorphic data is fundamental, and a range of technologies have been developed to address its acquisition needs.

[0003] With the rapid development of hardware technology, the detailed information captured about target scenes is becoming increasingly refined. Whether representing a scene using large-scale spatial point clouds or voxel scene grids, the resulting data volume is experiencing explosive growth. The rate of data growth has far outstripped the development of computer hardware.

[0004] Existing point cloud 3D scene modeling technologies are inefficient in utilizing hardware resources. This is especially true for the processing, storage, and display of large-scale point cloud data, which places high demands on hardware. However, the pace of hardware technology development has not kept pace with the growth in data volume. Therefore, there is an urgent need for an efficient, accurate, and resource-efficient method for storing large-scale point clouds and rapidly modeling scenes. Summary of the Invention

[0005] In order to solve the technical problems raised in the background technology, the purpose of the present invention is to provide a fast scene modeling method based on the linear storage structure of a three-dimensional sparse matrix of a point cloud.

[0006] The object of the present invention is achieved by comprising the following steps:

[0007] S100, data preprocessing: reordering the original disordered point cloud to form an ordered data set;

[0008] S200, constructing a linear indexing mechanism and scene detail level: constructing a linear indexing mechanism for ordered data using a linear octree index and a spatial data adjacent sorting mechanism, and establishing a scene detail level;

[0009] S300, point cloud data storage and retrieval: using the sparse octree data structure, point cloud data is stored and retrieved by dividing the space into layers.

[0010] Preferably, step S100 reorders the original unordered point cloud, including the following steps:

[0011] S101, spatially sorting the original point cloud data, using a pointer to point to the starting point of the child node array, and then using the offset for addressing to construct a node-coupled sparse octree to form an ordered data set;

[0012] S102: Use a flag variable to mark the child node status.

[0013] Preferably, step S200 uses a linear octree index and a spatial data adjacent sorting mechanism to construct a linear index mechanism for ordered data, specifically including the following steps:

[0014] S201, linearize the sparse octree structure to complete the scheduling of nodes and data contained in the tree structure;

[0015] S202: Constructing a vertex index pool and a dynamic linked list based on integer offsets. Reducing storage space requirements, achieving fast data retrieval and scheduling, and meeting high-frame-rate real-time rendering requirements require constructing a vertex index pool and a dynamic linked list based on integer offsets using a compact structure and an efficient indexing mechanism.

[0016] S203. Use index linked lists and Morton coding to sort and manage data.

[0017] Preferably, step S201 constructs an information domain for each sparse octree child node to control the expansion brought about by the increase in the depth of the tree; the information domain of the sparse octree child node includes a flag variable for indicating the node state, an RGB type variable for representing the brightness of the current node, a point count variable for specifying the total number of points in the current child node branch, and finally a memory union variable is used to encode a pointer to the child node, data, and the offset of the node in the memory.

[0018] Preferably, during the construction process of step S202, the 8-byte pointer is divided into two 4-byte variable structures, which are used to point to the offset of the next point in the file and the end of the index chain respectively.

[0019] Preferably, the index linked list in step S203 includes:

[0020] In the index linked list, each linked list node records the location information of a point cloud data;

[0021] Retrieve point cloud data by sequentially traversing the linked list nodes;

[0022] Use hash table to optimize the access speed of linked list.

[0023] Preferably, step S203 specifically includes the following steps:

[0024] S2031. Determine the content of the node descriptor and the continuous storage rules of the child node descriptors: In data organization, the head node uses a single descriptor, and the child nodes use an array to uniformly manage descendant nodes; in the file layout of the node descriptor, the nodes are stored in a continuous manner according to the specific implementation, where blank spaces are used to represent the hierarchical structure of the node coupling array and do not actually exist;

[0025] S2032. Determine the descriptor traversal rules and establish an index tree: The linear sparse octree information is recorded in the file header, and the node descriptors are recorded in the file in the traversal order to describe the nodes. Based on the actual computing power of the system, a flexible memory window is used to record the corresponding node table during the node search. The linear sparse octree is regarded as an index tree for fast scheduling of data sets. In addition, based on the built-in level of detail (LOD) information, LOD data access of different granularities can be achieved.

[0026] After the index tree is established, a corresponding management mechanism needs to be established for the data to prevent the LOD level of detail of the index tree from not meeting the corresponding presentation requirements, or to quickly search for the corresponding data for operation; in this invention, the data is stored in a separate file sequence;

[0027] S2033. Establish a data management mechanism: Use Morton codes to sort point cloud data to reduce the potential frequency of data exchange, and group and index point cloud data; the spatial proximity of Morton codes can be used to improve data retrieval efficiency, fully utilizing the spatial proximity of data to bring its own spatial LOD detail layer, with significant performance advantages; using Morton codes to sort point cloud data in leaf nodes is transparent and relies on index lists when accessed; after performing large-scale editing operations and causing fragmentation, the point cloud data in the leaf nodes are reorganized to ensure high-speed operation of the system.

[0028] Among them, due to the existence of the index link access mechanism, there is no mandatory requirement for the amount of data in the node, and simply uses spatial proximity; the Morton encoding of the three-dimensional leaf node matrix is ​​similar to the two-dimensional case.

[0029] Preferably, step S200 establishes the level of detail of the scene by: embedding level of detail information in the index tree to support access to level of detail data of different granularities; when the data of the index leaf node cannot meet the level of detail rendering requirements, retrieving the original data in the external memory for display;

[0030] The detail level information specifically includes: recording the detail level of each node in the node descriptor; dynamically adjusting the rendering detail level according to the viewpoint distance; using a lower detail level for areas far from the viewpoint to reduce the amount of calculation;

[0031] Retrieving the original data in the external memory specifically involves: retrieving high-resolution data in the external memory when the level of detail cannot meet the rendering requirements; preprocessing the high-resolution data to ensure its compatibility with existing data; and dynamically loading and unloading the high-resolution data according to rendering requirements.

[0032] Preferably, step S300 specifically includes the following steps:

[0033] Use sparse octree to perform spatial hierarchical partitioning of point cloud data;

[0034] Record node information in the node descriptor and store it in traversal order;

[0035] Multi-level detail management and fast data retrieval are achieved through the node hierarchy.

[0036] Preferably, the spatial hierarchical division includes the following steps:

[0037] Based on the sparse octree structure, the information domain of the child node is established;

[0038] Use page mechanism or bucket mechanism to store leaf node data;

[0039] Index non-leaf nodes and locate leaf nodes;

[0040] Dynamically adjust the loading and unloading of leaf nodes according to data access frequency.

[0041] Preferably, step S300 uses ray casting and three-dimensional Brensenham algorithm to perform data retrieval during the point cloud data storage and retrieval process; and establishes a data management and scheduling mechanism.

[0042] Preferably, ray casting and 3D Brensenham algorithm are used to perform data retrieval, specifically comprising the following steps:

[0043] S3011. Use a ray casting algorithm to determine the intersection of a ray and an octree node: First, set recursive test parameters, such as the distance to the nearest viewpoint to be tracked, the three-dimensional depth of the current test, and other parameters. Then, use the ray casting algorithm to perform a ray intersection test. The test starts from the top of the octree and tests the ray cube (ray bounding box). If an intersection is found, recursively go deeper until an intermediate layer cube is found or a leaf node is reached.

[0044] S3012. Use the three-dimensional Brensenham algorithm to sample and calculate the steps of rays in three-dimensional space: through the ray equation, based on the coordinate range of the test area and the step size in each direction, quickly calculate the grid; for the application of subsequent algorithms, each grid is finally represented by its center point; by using the coordinates of the center point, the insertion test is performed in the order from near to far in the subsequent links, and finally the nearest intersection point is obtained.

[0045] The 3D Brensenham algorithm converts the ray intersection test process into a point insertion test. The algorithm mainly includes an external exposure interface and a core process of a given point insertion test.

[0046] Preferably, in step S3011, the nodes in the parent node are tested, and the direction of the recursive depth is determined according to the test result.

[0047] The recursive traversal algorithm mainly includes the comparison of LOD of the intermediate points and the function of returning the point cloud data from the leaf node. According to the intersection test of the child nodes, the algorithm searches for the branch with the closest intersection for deep recursion.

[0048] Preferably, in establishing a data management and scheduling mechanism, point cloud data is dynamically scheduled based on the current visual range and user needs; an LRU (least recently used) mechanism is used to track and control the life cycle of nodes; memory usage is optimized to improve the response speed of data processing and rendering.

[0049] Compared with the prior art, the present invention has the following technical effects:

[0050] 1. The method of the present invention eliminates the addressing cost of traditional spatial data structures, reduces spatial complexity, and achieves efficient storage and rapid retrieval of large-scale point cloud data. The proposed rapid scene modeling method has significant advantages in data processing efficiency and rendering performance. It is suitable for scenarios requiring efficient spatial data processing and real-time rendering, such as wide-area-scale terrain data acquisition and analysis, and has good application prospects.

[0051] 3. The present invention uses the adjacency of spatial data to sort and manage, effectively reducing the time complexity of data processing and retrieval;

[0052] 4. The present invention adopts a three-dimensional sparse matrix storage algorithm, combined with information compression coding, which significantly reduces the storage requirements of point cloud data;

[0053] 5. The present invention adopts a flexible mechanism to allow the management and processing of large-scale data within limited memory; it schedules data in real time according to the current visual range, avoiding the need to load all data into memory;

[0054] 6. The present invention achieves flexible data scheduling under different CPU environments, more efficient point cloud rendering and calculation, especially in the application of terrain modeling and deformation monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of the process of the present invention;

[0056] Figure 2 It is a node-coupled sparse quadtree;

[0057] Figure 3 Vertex index pool and index list for leaf nodes;

[0058] Figure 4 It is the layout structure of the node descriptor;

[0059] Figure 5 The file structure of the index tree;

[0060] Figure 6 Morton encoding of an 8×8 two-dimensional matrix;

[0061] Figure 7 Schematic diagram of ray casting sampling for a plane mesh;

[0062] Figure 8 This is a diagram showing the rendering effect. DETAILED DESCRIPTION

[0063] The present invention is further described below with reference to the embodiments and drawings, but the present invention is not limited in any way. Any changes or substitutions made based on the teachings of the present invention fall within the scope of protection of the present invention.

[0064] Example 1

[0065] As attached Figure 1 As shown, the fast scene modeling method based on the linear storage structure of the point cloud three-dimensional sparse matrix in this embodiment includes the following steps:

[0066] S100, data preprocessing: reordering the original disordered point cloud to form an ordered data set;

[0067] S200, constructing a linear indexing mechanism and scene detail level: constructing a linear indexing mechanism for ordered data using a linear octree index and a spatial data adjacent sorting mechanism, and establishing a scene detail level;

[0068] S300, point cloud data storage and retrieval: using the sparse octree data structure, point cloud data is stored and retrieved by dividing the space into layers.

[0069] Example 2

[0070] The fast scene modeling method based on the linear storage structure of the point cloud three-dimensional sparse matrix in this embodiment is based on the embodiment 1. Step S100 reorders the original disordered point cloud, and includes the following steps:

[0071] S101. Sort the original point cloud data in space, as shown in the attached Figure 2 As shown, considering the sparsity of the matrix and subsequent operations, a node-coupled sparse octree is constructed by using a pointer to point to the starting point of the child node array and then using an offset for addressing to form an ordered data set;

[0072] S102. Use a flag variable to mark the status of the child node. In different operating environments or platforms, it is necessary to dynamically schedule the nodes according to the actual resources. Use the flag variable to promote the traversal, dynamic loading and unloading of data in the sparse octree.

[0073] Example 3

[0074] The fast scene modeling method based on the linear storage structure of the point cloud three-dimensional sparse matrix in this embodiment is based on the second embodiment. Step S200 uses a linear octree index and a spatial data adjacent sorting mechanism to construct a linear index mechanism for ordered data. Specifically, the following steps are included:

[0075] S201, linearize the sparse octree structure to complete the scheduling of nodes and data contained in the tree structure;

[0076] S202, constructing a vertex index pool and a dynamic linked list based on integer offsets;

[0077] S203. Use index linked lists and Morton coding to sort and manage data.

[0078] Example 4

[0079] The fast scene modeling method based on the linear storage structure of the three-dimensional sparse matrix of the point cloud in this embodiment is based on Example 3. Step S201 constructs an information domain for each sparse octree child node to control the expansion brought about by the increase in the depth of the tree; the information domain of the sparse octree child node includes a flag variable for indicating the node state, an RGB type variable "color" for representing the brightness of the current node, a point count variable for specifying the total number of points in the current child node branch, and finally a memory joint variable is used to encode a pointer to the child node, data, and the offset of the node in the memory.

[0080] Example 5

[0081] The fast scene modeling method based on the linear storage structure of the three-dimensional sparse matrix of the point cloud in this embodiment is based on Example 4. During the construction process of step S202, the 8-byte pointer is divided into two 4-byte variable structures, which are used to point to the offset of the next point in the file and the end of the index chain respectively.

[0082] Example 6

[0083] The fast scene modeling method based on the linear storage structure of the point cloud three-dimensional sparse matrix in this embodiment is based on the embodiment 5. The index linked table in step S203 includes:

[0084] In the index linked list, each linked list node records the location information of a point cloud data;

[0085] Retrieve point cloud data by sequentially traversing the linked list nodes;

[0086] Use hash table to optimize the access speed of linked list.

[0087] Example 7

[0088] As attached Figure 4 , Attachment Figure 5 , Attachment Figure 6 As shown, the fast scene modeling method based on the linear storage structure of the point cloud three-dimensional sparse matrix in this embodiment is based on Example 6, and step S203 specifically includes the following steps:

[0089] S2031. Determine the content of the node descriptor and the continuous storage rules for child node descriptors: In data organization, the head node uses a single descriptor, and the child nodes use arrays to uniformly manage descendant nodes. As shown in FIG4 , in the file layout of the node descriptor, nodes are stored in a continuous manner according to the specific implementation. Blank spaces are used to represent the hierarchical structure of the node coupling array and do not actually exist.

[0090] S2032. Determine the traversal rules of the descriptor and establish an index tree: the information of the linear sparse octree is recorded in the file header, and the node descriptors are recorded in the file in the traversal order to describe the nodes; according to the actual computing power of the system, a flexible memory window is used to record the corresponding node table in the node search; the linear sparse octree is regarded as an index tree for fast scheduling of data sets; the index tree structure is shown in the attached figure. Figure 5 As shown;

[0091] S2033. Establish data management mechanism: as attached Figure 6 As shown in Figure 1, Morton codes are used to sort point cloud data to reduce the potential exchange frequency of data and to group and index point cloud data. The spatial proximity of Morton codes can be used to improve data retrieval efficiency. Figure 6In the process, Morton encoding is performed on the point cloud data contained in the leaf nodes, which fully utilizes the spatial proximity of the data and brings its own spatial LOD detail layer, which has significant performance advantages; the use of Morton encoding to sort the point cloud data in the leaf nodes is transparent, and access relies on the index list; after performing large-scale editing operations and causing fragmentation, the point cloud data in the leaf nodes are reorganized to ensure the high-speed operation of the system.

[0092] Example 8

[0093] The rapid scene modeling method based on the linear storage structure of a three-dimensional sparse matrix of a point cloud in this embodiment is based on the seventh embodiment. The specific steps of establishing the level of detail of the scene in step S200 are as follows: the level of detail information is embedded in the index tree to support access to level of detail data of different granularities; when the data of the index leaf node cannot meet the level of detail rendering requirements, the original data in the external memory is retrieved for display;

[0094] The detail level information specifically includes: recording the detail level of each node in the node descriptor; dynamically adjusting the rendering detail level according to the viewpoint distance; using a lower detail level for areas far from the viewpoint to reduce the amount of calculation;

[0095] Retrieving the original data in the external memory specifically involves: retrieving high-resolution data in the external memory when the level of detail cannot meet the rendering requirements; preprocessing the high-resolution data to ensure its compatibility with existing data; and dynamically loading and unloading the high-resolution data according to rendering requirements.

[0096] Example 9

[0097] The fast scene modeling method based on the linear storage structure of the point cloud three-dimensional sparse matrix in this embodiment is based on Example 8, and step S300 specifically includes the following steps:

[0098] Use sparse octree to perform spatial hierarchical partitioning of point cloud data;

[0099] Record node information in the node descriptor and store it in traversal order;

[0100] Multi-level detail management and data retrieval are performed through the node hierarchy.

[0101] Example 10

[0102] The fast scene modeling method based on the linear storage structure of the point cloud three-dimensional sparse matrix in this embodiment is based on the ninth embodiment. The spatial hierarchical division includes the following steps:

[0103] Based on the sparse octree structure, the information domain of the child node is established;

[0104] Use page mechanism or bucket mechanism to store leaf node data;

[0105] Index non-leaf nodes and locate leaf nodes;

[0106] Dynamically adjust the loading and unloading of leaf nodes according to data access frequency.

[0107] Example 11

[0108] The fast scene modeling method based on the linear storage structure of the three-dimensional sparse matrix of the point cloud in this embodiment is based on Example 10. In step S300, ray casting and the three-dimensional Brensenham algorithm are used to perform data retrieval during the point cloud data storage and retrieval process; a data management and scheduling mechanism is established.

[0109] Example 12

[0110] As attached Figure 7 As shown, the fast scene modeling method based on the linear storage structure of the point cloud three-dimensional sparse matrix in this embodiment is based on Example 11, and ray casting and three-dimensional Brensenham algorithm are used for data retrieval, specifically including the following steps:

[0111] S3011. Use the ray casting algorithm to determine the intersection of the ray and the octree node: first, set the recursive test parameters, then use the ray casting algorithm to perform the ray intersection test. During the test, the ray cube test is performed from the top of the octree. If there is an intersection, recursively go deeper until the middle layer cube is found or the leaf node is reached;

[0112] S3012. Use the three-dimensional Brensenham algorithm to sample and calculate the stepping of rays in three-dimensional space: calculate the grid based on the ray equation, the coordinate range of the test area and the step length in each direction; for the application of subsequent algorithms, each grid is finally represented by its center point; by using the coordinates of the center point, the insertion test is performed in the order from near to far in the subsequent links, and finally the nearest intersection point is obtained.

[0113] Example 13

[0114] The fast scene modeling method based on the linear storage structure of the point cloud three-dimensional sparse matrix in this embodiment is based on Example 12. In step S3011, the nodes in the parent node are tested, and the direction of the recursive depth is determined according to the test results.

[0115] Example 14

[0116] The fast scene modeling method based on the linear storage structure of the three-dimensional sparse matrix of the point cloud in this embodiment is based on Example 13. In establishing a data management and scheduling mechanism, point cloud data is dynamically scheduled based on the current visual range and user needs; an LRU mechanism is used to track and control the life cycle of nodes; and memory usage is optimized to improve the response speed of data processing and rendering.

[0117] Example 14

[0118] The effect of rendering the scene using the multi-viewpoint 3D laser point cloud data collected in a certain area according to the method of Example 13 is shown in the attached figure. Figure 8 shown.

Claims

1. A fast scene modeling method based on the linear storage structure of point cloud three-dimensional sparse matrix, characterized by The following steps are involved: S100, data preprocessing: reordering the original disordered point cloud to form an ordered data set; S200, constructing a linear indexing method and scene detail level: constructing a linear indexing method for ordered data using a linear octree index and a spatial data adjacent sorting method, and establishing a scene detail level; S300, point cloud data storage and retrieval: using the sparse octree data structure, point cloud data is stored and retrieved by dividing the space into layers; The method of constructing a linear index for ordered data using a linear octree index and a spatial data adjacent sorting method in step S200 specifically includes the following steps: S201, linearize the sparse octree structure to complete the scheduling of nodes and data contained in the tree structure; S202, constructing a vertex index pool and a dynamic linked list based on integer offsets; S203, sorting and managing data using index linked lists and Morton coding; The step S200 establishes the level of detail of the scene by: embedding the level of detail information in the index tree to support access to level of detail data of different granularities; when the data of the index leaf node cannot meet the level of detail rendering requirements, retrieving the original data in the external memory for display; The detail level information specifically includes: recording the detail level of each node in each node descriptor; dynamically adjusting the rendering detail level according to the viewpoint distance; Retrieving the original data in the external memory specifically includes: retrieving high-resolution data in the external memory when the level of detail cannot meet the rendering requirements; pre-processing the high-resolution data; dynamically loading and unloading the high-resolution data according to the rendering requirements; Step S300 specifically includes the following steps: Use sparse octree to perform spatial hierarchical partitioning of point cloud data; Record node information in the node descriptor and store it in traversal order; Multi-level detail management and data retrieval through the node hierarchy; The spatial hierarchical division comprises the following steps: Based on the sparse octree structure, the information domain of the child node is established; Use pages or buckets to store leaf node data; Index non-leaf nodes and locate leaf nodes; Dynamically adjust the loading and unloading of leaf nodes according to data access frequency.

2. The rapid scene modeling method based on the point cloud three-dimensional sparse matrix linear storage structure according to claim 1 is characterized in that Step S100 reorders the original unordered point cloud, including the following steps: S101, spatially sorting the original point cloud data, using a pointer to point to the starting point of the child node array, and then using the offset for addressing to construct a node-coupled sparse octree to form an ordered data set; S102: Use a flag variable to mark the child node status.

3. The rapid scene modeling method based on the linear storage structure of point cloud three-dimensional sparse matrix according to claim 1 is characterized in that Step S201 constructs an information domain for each sparse octree child node to control the expansion as the depth of the tree increases; The information domain of a sparse octree child node includes a flag variable for indicating the node status, an RGB type variable for representing the brightness of the current node, a point count variable for specifying the total number of points in the current child node branch, and finally a memory union variable is used to encode a pointer to the child node, data, and the offset of the node in memory.

4. The rapid scene modeling method based on the linear storage structure of point cloud three-dimensional sparse matrix according to claim 1 is characterized in that During the construction process of step S202, the 8-byte pointer is divided into two 4-byte variable structures, which are used to point to the offset of the next point in the file and the end of the index chain respectively.

5. The rapid scene modeling method based on the linear storage structure of point cloud three-dimensional sparse matrix according to claim 1 is characterized in that The index linked list in step S203 includes: In the index linked list, each linked list node records the location information of a point cloud data; Retrieve point cloud data by sequentially traversing the linked list nodes; Use hash table to optimize the access speed of linked list.

6. The rapid scene modeling method based on the point cloud three-dimensional sparse matrix linear storage structure according to claim 1 is characterized in that Step S203 specifically includes the following steps: S2031. Determine the content of the node descriptor and the continuous storage rules of the child node descriptors: In data organization, the head node uses a single descriptor, and the child nodes use an array to uniformly manage descendant nodes; in the file layout of the node descriptor, the nodes are stored in a continuous manner according to the specific implementation, where blank spaces are used to represent the hierarchical structure of the node coupling array and do not actually exist; S2032. Determine the descriptor traversal rules and establish an index tree: the linear sparse octree information is recorded in the file header, and the node descriptors are recorded in the file in the traversal order to describe the nodes; based on the actual computing power of the system, a flexible memory window is used to record the corresponding node table during the node search; the linear sparse octree is regarded as an index tree for fast scheduling of data sets; S2033. Perform data management: Use Morton codes to sort the point cloud data, group the point cloud data, and index the point cloud data; after performing large-scale editing operations and causing fragmentation, reorganize the point cloud data in the leaf nodes.

7. The rapid scene modeling method based on the linear storage structure of point cloud three-dimensional sparse matrix according to claim 1 is characterized in that Step S300 uses ray casting and three-dimensional Brensenham algorithm to perform data retrieval during the point cloud data storage and retrieval process; and performs data management and scheduling.

8. The rapid scene modeling method based on the linear storage structure of point cloud three-dimensional sparse matrix according to claim 7 is characterized in that Ray casting and the 3D Brensenham algorithm are used for data retrieval, which includes the following steps: S3011. Use the ray casting algorithm to determine the intersection of the ray and the octree node: first, set the recursive test parameters, then use the ray casting algorithm to perform the ray intersection test. During the test, the ray cube test is performed from the top of the octree. If there is an intersection, recursively go deeper until the middle layer cube is found or the leaf node is reached; S3012. Use the three-dimensional Brensenham algorithm to sample and calculate the step size of rays in three-dimensional space: calculate the grid based on the ray equation, the coordinate range of the test area and the step size in each direction; each grid is finally represented by its center point; by using the coordinates of the center point, insert the test in the order from near to far, and finally get the nearest intersection point.

9. The rapid scene modeling method based on the point cloud three-dimensional sparse matrix linear storage structure according to claim 8, characterized in that In step S3011, the nodes in the parent node are tested, and the direction of the recursive depth is determined according to the test result.

10. The rapid scene modeling method based on the point cloud three-dimensional sparse matrix linear storage structure according to claim 7, characterized in that In data management and scheduling, point cloud data is dynamically scheduled based on the current visual range and user needs; the LRU method is used to track and control the life cycle of nodes; Optimize memory usage and improve the responsiveness of data processing and rendering.

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