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• W3205821132 startingPage "9581" @default.
• W3205821132 abstract "The spatial index structure is one of the most important research topics for organizing and managing massive 3D Point Cloud. As a point in Point Cloud consists of Cartesian coordinates (x,y,z), the common method to explore geometric information and features is nearest neighbor searching. An efficient spatial indexing structure directly affects the speed of the nearest neighbor search. Octree and kd-tree are the most used for Point Cloud data. However, octree or KD-tree do not perform best in nearest neighbor searching. A highly balanced tree, 3D R*-tree is considered the most effective method so far. So, a hybrid spatial indexing structure is proposed based on octree and 3D R*-tree. In this paper, we discussed how thresholds influence the performance of nearest neighbor searching and constructing the tree. Finally, an adaptive way method adopted to set thresholds. Furthermore, we obtained a better performance in tree construction and nearest neighbor searching than octree and 3D R*-tree." @default.
• W3205821132 created "2021-10-25" @default.
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• W3205821132 date "2021-10-14" @default.
• W3205821132 modified "2023-09-25" @default.
• W3205821132 title "A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree" @default.
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• W3205821132 doi "https://doi.org/10.3390/app11209581" @default.
• W3205821132 hasPublicationYear "2021" @default.
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