Siyong Fu, Lushen Wu. Feature Extraction from 3D Point Clouds Based on Linear Intercept Ratio[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091009
- Laser & Optoelectronics Progress
- Vol. 56, Issue 9, 091009 (2019)
Fig. 1. Schematic of linear intercept
Fig. 2. Linear intercepts between two points for different cases. (a) dp01 for feature point of p0; (b) dp10for non-feature point of p1; (c) d'p01 for feature point of p0; (d) dp12, dp21 for non-feature points of p1 and p2
Fig. 3. Phenomenon of misjudgment
Fig. 4. Feature points of Fandisk model. (a) Original model; (b) model reduced by 60%; (c) model with 20 dB noise
Fig. 5. Feature points of Bunny model. (a) Original model; (b) model with 10 dB noise; (c) model reduced by 60%
Fig. 6. Package diagram of model prototype. (a) Cross cuboid model; (b) workpiece model
Fig. 7. Feature points of cross cuboid model extracted under different δ values. (a) δ=10; (b) δ=9; (c) δ=8; (d) δ=7; (e) δ=6; (f) δ=5; (g) δ=4; (h) δ=3; (i) δ=2; (j) δ=1
Fig. 8. Feature points of workpiece model extracted under different δ values. (a) δ=10; (b) δ=9; (c) δ=8; (d) δ=7; (e) δ=6; (f) δ=5; (g) δ=4; (h) δ=3; (i) δ=2; (j) δ=1
Fig. 9. Extraction results of MSSV method under different intensities of noise. (a) 0 dB; (b) 5 dB; (c) 10 dB; (d) 15 dB
Fig. 10. Extraction results of NASD method under different intensities of noise. (a) 0 dB; (b) 5 dB; (c) 10 dB; (d) 15 dB
Fig. 11. Extraction results of proposed method under different intensities of noise. (a) 0 dB; (b) 5 dB; (c) 10 dB; (d) 15 dB
Fig. 12. Number of extracted feature points under different intensities of noise
Fig. 13. Extraction results of MSSV method. (a) Original model; (b) model reduced by 10%; (c) model reduced by 30%; (d)model reduced by 50%; (e) model reduced by 70%
Fig. 14. Extraction results of NASD method. (a) Original model ; (b) model reduced by 10%; (c) model reduced by 30%; (d) model reduced by 50%; (e) model reduced by 70%
Fig. 15. Extraction results of proposed method. (a) Original model; (b) model reduced by 10%; (c) model reduced by 30%; (d) model reduced by 50%; (e) model reduced by 70%
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Table 1. Number of feature points and computation time
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