(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.
Multi-photon neuron embedded bionic skin for high-precision complex texture and object reconstruction perception research
Hongyu Zhou, Chao Zhang, Hengchang Nong, Junjie Weng, Dongying Wang, Yang Yu, Jianfa Zhang, Chaofan Zhang, Jinran Yu, Zhaojian Zhang, Huan Chen, Zhenrong Zhang, Junbo Yang
Opto-Electronic Advances
2025-01-22
