Laser & Optoelectronics Progress, Volume. 60, Issue 24, 2410011(2023)
Fracture Zone Extraction Method Based on Three-Dimensional Convolutional Neural Network Combined with PointSIFT
Fig. 1. Flow chart of LiDAR point cloud fracture zone extraction method
Fig. 2. Schematic diagram of OE convolution unit. (a) Point cloud in 3D space (the input point is at origin); (b) nearest neighbour search in eight octants; (c) convolution along X, Y, Z axis
Fig. 3. PointSIFT module
Fig. 4. Schematic diagram of three-dimensional convolution module framework
Fig. 5. Schematic diagram of PS-CNN point cloud fracture zone extraction framework
Fig. 6. Sample display diagrams of ISPRS point cloud datasets. (a); Samp51; (b) Samp53
Fig. 7. Sample display diagrams of Chuandian point cloud datasets. (a) CD_1; (b) CD_2
Fig. 8. Results of three fracture zone extraction methods on Samp51. (a) Label; (b) TD; (c) DNN; (d) PS-CNN
Fig. 9. Results of three fracture zone extraction methods on Samp53. (a) Label; (b) TD; (c) DNN; (d) PS-CNN
Fig. 10. Results of three fracture zone extraction methods on CD_1. (a) Label; (b) TD; (c) DNN; (d) PS-CNN
Fig. 11. Results of three fracture zone extraction methods on CD_2. (a) Label; (b) TD; (c) DNN; (d) PS-CNN
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Hao Wang, Dongmei Song, Bin Wang, Song Dai. Fracture Zone Extraction Method Based on Three-Dimensional Convolutional Neural Network Combined with PointSIFT[J]. Laser & Optoelectronics Progress, 2023, 60(24): 2410011
Category: Image Processing
Received: Feb. 27, 2023
Accepted: May. 15, 2023
Published Online: Dec. 4, 2023
The Author Email: Wang Hao (z20160115@s.upc.edu.cn)