Acta Optica Sinica, Volume. 37, Issue 11, 1115007(2017)
Point Cloud Simplification Method Based on Space Grid Dynamic Partitioning
Fig. 1. Schematic of dynamic division
Fig. 2. (a) Original point cloud and (b) feature points
Fig. 3. Simplification results. (a) Reduced by 35.98%; (b) reduced by 65.23%; (c) reduced by 78.12%; (d) reduced by 85.41%
Fig. 4. Simplification results of random sampling method. (a) Reduced by 50%; (b) reduced by 75%; (c) reduced by 87.5%; (d) reduced by 93.75%
Fig. 5. Simplification results of grid method. (a) Reduced by 51.2%; (b) reduced by 75.1%; (c) reduced by 87.43%; (d) reduced by 93.66%
Fig. 6. Simplification results of curvature method. (a) Reduced by 50%; (b) reduced by 75%; (c) reduced by 87.5%; (d) reduced by 93.75%
Fig. 7. Simplification results of proposed method. (a) Reduced by 51.5%; (b) reduced by 75.08%; (c) reduced by 87.53%; (d) reduced by 93.73%
Fig. 8. Simplified error comparison. (a) Maximum error; (b) average error
|
Get Citation
Copy Citation Text
Siyong Fu, Lushen Wu, Huawei Chen. Point Cloud Simplification Method Based on Space Grid Dynamic Partitioning[J]. Acta Optica Sinica, 2017, 37(11): 1115007
Category: Machine Vision
Received: Jun. 9, 2017
Accepted: --
Published Online: Sep. 7, 2018
The Author Email: Fu Siyong (fusiyong58@163.com)