(Peer-Reviewed) Deep-learning-enabled dual-frequency composite fringe projection profilometry for single-shot absolute 3D shape measurement
Yixuan Li 李奕萱 ¹ ², Jiaming Qian 钱佳铭 ¹ ², Shijie Feng 冯世杰 ¹ ², Qian Chen 陈钱 ¹ ², Chao Zuo 左超 ¹ ²
¹ Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing 210094, China
中国 南京 南京理工大学智能计算成像实验室
² Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China
中国 南京 南京理工大学 江苏省光谱成像与智能感知重点实验室
Opto-Electronic Advances, 2022-03-10
Abstract
Single-shot high-speed 3D imaging is important for reconstructions of dynamic objects. For fringe projection profilometry (FPP), however, it is still challenging to recover accurate 3D shapes of isolated objects by a single fringe image.
In this paper, we demonstrate that the deep neural networks can be trained to directly recover the absolute phase from a unique fringe image that involves spatially multiplexed fringe patterns of different frequencies. The extracted phase is free from spectrum-aliasing problem which is hard to avoid for traditional spatial-multiplexing methods.
Experiments on both static and dynamic scenes show that the proposed approach is robust to object motion and can obtain high-quality 3D reconstructions of isolated objects within a single fringe image.
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