Abstract

High dynamic range (HDR) 3D measurement is a meaningful but challenging problem. Recently, many deep-learning-based methods have been proposed for the HDR problem. However, due to learning redundant fringe intensity information, their networks are difficult to converge for data with complex surface reflectivity and various illumination conditions, resulting in non-robust performance. To address this problem, we propose a physics-based supervised learning method. By introducing the physical model for phase retrieval, we design a novel, to the best of our knowledge, sinusoidal-component-to-sinusoidal-component mapping paradigm. Consequently, the scale difference of fringe intensity in various illumination scenarios can be eliminated. Compared with conventional supervised-learning methods, our method can greatly promote the convergence of the network and the generalization ability, while compared with the recently proposed unsupervised-learning method, our method can recover complex surfaces with much more details. To better evaluate our method, we specially design the experiment by training the network merely using the metal objects and testing the performance using different diffuse sculptures, metal surfaces, and their hybrid scenes. Experiments for all the testing scenarios have high-quality phase recovery with an STD error of about 0.03 rad, which reveals the superior generalization ability for complex reflectivity and various illumination conditions. Furthermore, the zoom-in 3D plots of the sculpture verify its fidelity on recovering fine details.

© 2024 Optica Publishing Group

Semi-supervised angular super-resolution method for autostereoscopic 3D surface measurement

Sanshan Gao, Chi Fai Cheung, and Da Li
Opt. Lett. 49(4) 858-861 (2024)

Efficient automated high dynamic range 3D measurement via deep reinforcement learning

Pan Zhang, Kai Zhong, Zhongwei Li, and Yusheng Shi
Opt. Express 32(4) 4857-4875 (2024)

Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry

Jiaming Qian, Shijie Feng, Yixuan Li, Tianyang Tao, Jing Han, Qian Chen, and Chao Zuo
Opt. Lett. 45(7) 1842-1845 (2020)

Unsupervised spectral reconstruction from RGB images under two lighting conditions

Xuheng Cao, Yusheng Lian, Zilong Liu, Jin Li, and Kaixuan Wang
Opt. Lett. 49(8) 1993-1996 (2024)

Global-optimal semi-supervised learning for single-pixel image-free sensing

Xinrui Zhan, Hui Lu, Rong Yan, and Liheng Bian
Opt. Lett. 49(3) 682-685 (2024)