Abstract

Single-shot 3D shape reconstruction integrating structured light and deep learning has drawn considerable attention and achieved significant progress in recent years due to its wide-ranging applications in various fields. The prevailing deep-learning-based 3D reconstruction using structured light generally transforms a single fringe pattern to its corresponding depth map by an end-to-end artificial neural network. At present, it remains unclear which kind of structured-light patterns should be employed to obtain the best accuracy performance. To answer this fundamental and much-asked question, we conduct an experimental investigation of six representative structured-light patterns adopted for single-shot 2D-to-3D image conversion. The assessment results provide a valuable guideline for structured-light pattern selection in practice.

© 2022 Optica Publishing Group

Single-shot 3D shape acquisition using a learning-based structured-light technique

Andrew-Hieu Nguyen, Khanh L. Ly, Charlotte Qiong Li, and Zhaoyang Wang
Appl. Opt. 61(29) 8589-8599 (2022)

MIMONet: Structured-light 3D shape reconstruction by a multi-input multi-output network

Hieu Nguyen, Khanh L. Ly, Thanh Nguyen, Yuzheng Wang, and Zhaoyang Wang
Appl. Opt. 60(17) 5134-5144 (2021)

Composite fringe projection deep learning profilometry for single-shot absolute 3D shape measurement

Yixuan Li, Jiaming Qian, Shijie Feng, Qian Chen, and Chao Zuo
Opt. Express 30(3) 3424-3442 (2022)

Depth estimation from a single-shot fringe pattern based on DD-Inceptionv2-UNet

Linlin Wang, Wenke Xue, Chuanyun Wang, Qian Gao, Wenjie Liang, and Yinwen Zhang
Appl. Opt. 62(34) 9144-9155 (2023)

Dual-stage hybrid network for single-shot fringe projection profilometry based on a phase-height model

Xuwen Song and Lianpo Wang
Opt. Express 32(1) 891-906 (2024)