PhotoniX, Volume. 5, Issue 1, 25(2024)
Deep-learning-enabled temporally super-resolved multiplexed fringe projection profilometry: high-speed kHz 3D imaging with low-speed camera
[9] [9] Caspar S, Honegger M, Rinner S, Lambelet P, Bach C, Ettemeyer A. High speed fringe projection for fast 3D inspection. In: Optical Measurement Systems for Industrial Inspection VII. vol. 8082. SPIE; 2011. p. 298–304.
[14] [14] Zuo C, Tao T, Feng S, Huang L, Asundi A, Chen Q. Micro Fourier transform profilometry (μFTP): 3D shape measurement at 10,000 frames per second. Optics Lasers Eng. 2018;102:70–91.
[33] [33] Weise T, Leibe B, Van Gool L. Fast 3D Scanning with Automatic Motion Compensation. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE; 2007. pp. 1–8.
[35] [35] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer; 2015. p. 234–241.
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Wenwu Chen, Shijie Feng, Wei Yin, Yixuan Li, Jiaming Qian, Qian Chen, Chao Zuo. Deep-learning-enabled temporally super-resolved multiplexed fringe projection profilometry: high-speed kHz 3D imaging with low-speed camera[J]. PhotoniX, 2024, 5(1): 25
Category: Research Articles
Received: May. 7, 2024
Accepted: Aug. 2, 2024
Published Online: Jan. 23, 2025
The Author Email: Feng Shijie (shijiefeng@njust.edu.cn), Chen Qian (chenqian@njust.edu.cn), Zuo Chao (zuochao@njust.edu.cn)