Nguyen Andrew-Hieu, Sun Brian, Li Charlotte Qiong, Wang Zhaoyang
Appl Opt. 2022 Dec 1;61(34):10105-10115. doi: 10.1364/AO.468984.
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.
近年来，结合结构光和深度学习的单镜头三维形状重建因其在各个领域的广泛应用而备受关注，并取得了显著进展。目前流行的基于深度学习的使用结构光的三维重建通常通过端到端人工神经网络将单个条纹图案转换为其对应的深度图。目前，尚不清楚应采用哪种结构光图案以获得最佳精度性能。为了回答这个基本且备受关注的问题，我们对用于单镜头二维到三维图像转换的六种代表性结构光图案进行了实验研究。评估结果为实际应用中的结构光图案选择提供了有价值的指导。
