光学学报, 2017, 37 (12): 1210003, 网络出版: 2018-09-06
基于改进八叉树的三维点云压缩算法 
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Three-Dimensional Point Cloud Compression Algorithm Based on Improved Octree
图 & 表
图 2. 包围盒示意图及局部放大细节。(a)水壶模型包围盒示意图;(b)水壶模型的局部放大细节;(c)兔子模型包围盒示意图;(d)兔子模型的局部放大细节
Fig. 2. Bounding box schematic and local enlargement details. (a) Bounding box schematic of kettle model; (b) local enlargement details of kettle model; (c) bounding box schematic of bunny model; (d) local enlargement details of bunny model
图 3. 八叉树分割,每个体素至少含一个点。(a) n=1, Pvoxel=9; (b) n=2, Pvoxel=40; (c) n=3, Pvoxel=137; (d) n=4, Pvoxel=435; (e)n=5, Pvoxel=1426; (f) n=6, Pvoxel=4815; (g) n=7, Pvoxel=15845; (h) n=8, Pvoxel=45859
Fig. 3. Octree segmentation, each voxel containing at least one point. (a) n=1, Pvoxel=9; (b) n=2, Pvoxel=40; (c) n=3, Pvoxel=137; (d) n=4, Pvoxel=435; (e)n=5, Pvoxel=1426; (f) n=6, Pvoxel=4815; (g) n=7, Pvoxel=15845; (h) n=8, Pvoxel=45859
图 6. 实验所用点云模型。(a)兔子模型,点数为31607;(b)猫模型,点数为10000;(c)水壶模型,点数为19062;(d)人脸模型,点数为46111;(e)塑料模型,点数为24673
Fig. 6. Point cloud models used in experiment. (a) Bunny model, point is 31607; (b) cat model, point is 10000; (c) kettle model, point is 19062; (d) face model, point is 46111; (e) plastic model, point is 24673
图 7. 离群点移除结果。(a)原始数据;(b)移除离群点后的点云数据;(c)移除的离群点
Fig. 7. Results of removing outliers. (a) Initial data; (b) point cloud data after removing outliers; (c) removed outliers
图 8. 兔子模型解压缩示意图。(a)解压缩前的兔子模型;(b)图(a)局部放大细节;(c) t=0.25时解压缩结果;(d)图(c)局部放大细节;(e) t=0.005时解压缩结果;(f)图(e)局部放大细节
Fig. 8. Decompression schematic of bunny model. (a) Bunny model before decompression; (b) local amplification details of Fig. (a); (c) decompression result when t=0.25; (d) local amplification details of Fig. (c); (e) decompression result when t=0.005; (f) local amplification details of Fig. (e)
表 1八叉树分割
Table1. Octree segmentation
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黄源, 达飞鹏, 唐林. 基于改进八叉树的三维点云压缩算法[J]. 光学学报, 2017, 37(12): 1210003. Yuan Huang, Feipeng Da, Lin Tang. Three-Dimensional Point Cloud Compression Algorithm Based on Improved Octree[J]. Acta Optica Sinica, 2017, 37(12): 1210003.
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