Chinese Optics, Volume. 17, Issue 1, 118(2024)
A hybrid network based on light self-limited attention for structured light phase and depth estimation
Fig. 4. Sample maps in some datasets. The first lines are simulation data, the second lines are real data. (a) Simulation fringe map; (b) simulation fringe map D; (c) simulation fringe map M; (d) simulation fringe wrapped phase; (e) real fringe map; (f) real fringe map D; (g) real fringe map M; (h) real fringe wrapped phase
Fig. 5. Comparison of different network simulation and real data wrapped phases. The blue boxes are the simulation data, and the orange boxes are the real data. (a) UNet; (b) DPH; (c) R2UNet; (d) SUNet; (e) Ours; (f) Label
Fig. 6. Wrapped phase curves.(a) Comparison of simulation data; (b) comparison of real data
Fig. 7. Flowchart of dataset generation. (a) Model import; (b) adjust of the model size; (c) projection fringe
Fig. 8. Sample maps in the dataset. (a) Simulated fringe map; (b) real fringe map; (c) simulation depth map; (d) real depth map
Fig. 9. Comparison of the visual results of depth estimation by different methods. The blue boxes are the simulation data, and the orange boxes are the real data. (a) Input data; (b) UNet; (c) DPH; (d) R2UNet; (e) Ours; (f) Label
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Xin-jun ZHU, Hao-miao ZHAO, Hong-yi WANG, Li-mei SONG, Rui-qun SUN. A hybrid network based on light self-limited attention for structured light phase and depth estimation[J]. Chinese Optics, 2024, 17(1): 118
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Received: Apr. 14, 2023
Accepted: --
Published Online: Mar. 28, 2024
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