Learning-based invalid points detection for fringe projection profilometry

Luo, Xiaolong; Bai, Songlin; Song, Wanzhong

doi:10.1117/12.2627287

Cited by 2 publications

(2 citation statements)

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“…In order to solve the problem of insufficient global learning ability of the model, Yan et al and Luo et al [20,21] proposed to use deep learning algorithms to recover the real phase map. Zhao et al [9] removed the points where the projector image coordinates did not meet the epipolar constraint.…”

Section: Related Workmentioning

confidence: 99%

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A Markov random field based method for removing invalid unwrapping phase points in 3D reconstruction

Gao

Cheng

et al. 2023

IET Image Processing

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Fringe projection profilometry is widely used in 3D structured light due to its fast speed and accuracy. However, in the process of phase unwrapping, it is easy to cause invalid points in the edges and shadows of objects, which leads to error points in 3D reconstruction. To solve this problem, we propose an invalid points removal method based on Markov random fields. Specifically, the proposed method formulates unwrapped phase and mask maps as energy functions and uses iterative methods to minimize them. Furthermore, we validate the proposed method in a monocular structured light system and compare it with existing algorithms. Results show that the proposed method effectively identifies edges and shadows while preserving valid points, and has strong robustness and correctness.

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Section: Related Workmentioning

confidence: 99%

“…and Luo et al. [20, 21] proposed to use deep learning algorithms to recover the real phase map. Zhao et al.…”

Section: Related Workmentioning

confidence: 99%