Mass point cloud layered real-time rendering method based on octree index

Abstract

The invention discloses a massive point cloud layered real-time rendering method based on an octree index. The three-dimensional laser scanning data volume often reaches a GB level or even a TB level, and if point clouds cannot be refreshed in real time, the requirement for visualization of massive point clouds cannot be met. The method comprises the six steps of point cloud three-dimensional grid partitioning, tiny point cloud block fusion in combination with octree features, point cloud partitioning binary system bin file dynamic output, point cloud partitioning binary system bin file multi-level index construction, Poisson disc uniform sampling, hierarchical structure output and point cloud real-time hierarchical scheduling rendering. According to the method, the point cloud file is subjected to three-dimensional grid partitioning, so that the memory consumption is greatly reduced, and the index query efficiency is improved; and an improved Poisson disk sampling strategy is used, so that data redundancy is avoided.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional laser scanning data processing, and relates to a layered real-time rendering method of massive point clouds based on an octree index. Background technique [0002] At present, 3D laser scanning technology can be used in digital protection of cultural relics, civil engineering, industrial measurement, natural disaster investigation, digital urban terrain visualization, urban and rural planning and other fields. The data volume often reaches GB or even TB level, while the commonly used computer memory is only 4-16GB , the point cloud data cannot be loaded at one time, and massive point clouds need to be managed and scheduled. At present, KD tree, R tree, quadtree, octree, etc. are commonly used, and combined with the pyramid structure to build indexes for point clouds, and the quality of index construction directly affects the organization and query efficiency of point cloud data. If point...

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Problems solved by technology

[0002] At present, 3D laser scanning technology can be used in digital protection of cultural relics, civil engineering, industrial measurement, natural disaster investigation, digital city terrain visualization, urban and rural planning and other fields. The data volume often reaches GB or even TB level, while the commonly used computer memory is only 4-16GB , the point cloud data cannot be loaded at one time, and massive point clouds need to be managed and scheduled

At present, KD tree, R tree, quadtree, octree, etc. are commonly used, and combined with the pyramid structure to build indexes for point clouds, and the quality of index construction directly affects the organization and query efficiency of point cloud data. If point clouds cannot Real-time refresh cannot meet the needs of massive point cloud visualization

[0003] Patent CN104750854B discloses a massive 3D laser point cloud compression storage and fast loading display method. By collecting the overall structure of point cloud data, the data is classified and compressed according to different levels. Each level includes block sets, blocks and packages. Three-level index , to achieve a high degree of storage and fast loading display, but it does not consider the data size of the point cloud file and the characteristics of the sparse distribution of the point cloud when dividing the point cloud, which will cause a lot of empty packets and memory waste, etc.

[0004] Patents CN106407408B, CN105808672, etc. disclose a method and device for constructing a spatial index of massive point cloud data. The obtained original point cloud data is divided into blocks to obtain multiple point cloud data blocks; for each point cloud data block, construct the current The octree index of the point cloud data block; the octree index of multiple point cloud data blocks is merged to obtain the spatial index structure of the original point cloud data, but in the process of establishing the point cloud octree pyramid structure, There is an intersection between different layers, resulting in data redundancy; and it uses a random sampling method, resulting in uneven point cloud data

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