Laser & Optoelectronics Progress, Volume. 60, Issue 16, 1615002(2023)
MSPoint: Point Cloud Denoising Network Based on Multiscale Distribution Score
Fig. 1. Overall network structure
Fig. 2. Feature extraction module
Fig. 3. Displacement prediction module
Fig. 4. Relation between score and denoising effect
Fig. 5. Score estimation unit
Fig. 6. Effect of different loss functions on point cloud denoising results. (a)
Fig. 7. Point cloud dataset. (a) block; (b) blade; (c) column; (d) joint; (e) casting; (f) cube; (g) fandisk
Fig. 8. Schematic diagrams of point cloud neighborhood extraction. (a) joint; (b) blade; (c) block; (d) column
Fig. 9. Comparison of 0.5% noise casting point cloud model denoising results
Fig. 10. Comparison of 0.5% noise cube point cloud model denoising results
Fig. 11. Comparison of 0.5% noise fandisk point cloud model denoising results
Fig. 12. Point Cloud data of a university library
Fig. 13. Comparison of gate steps before and after noise removal. (a) Before noise removal; (b) after noise removal
Fig. 14. Comparison of back corridor before and after noise removal. (a) Before noise removal; (b) after noise removal
Fig. 15. Comparison of corner before and after noise removal. (a) Before noise removal; (b) after noise removal
Fig. 16. Loss convergence of disturbances of different degrees. (a)
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Hao Hu, Qibing Wang, Jiawei Lu, Hongye Su, Jiankun Lai, Gang Xiao. MSPoint: Point Cloud Denoising Network Based on Multiscale Distribution Score[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1615002
Category: Machine Vision
Received: Aug. 29, 2022
Accepted: Oct. 13, 2022
Published Online: Aug. 18, 2023
The Author Email: Hu Hao (huhao0127@yeah.net), Xiao Gang (xg@zjut.edu.cn)