Li Yueyang, Wu Zhouejie, Shen Junfei, Zhang Qican
Opt Express. 2023 Nov 20;31(24):40803-40823. doi: 10.1364/OE.506343.
Achieving real-time and high-accuracy 3D reconstruction of dynamic scenes is a fundamental challenge in many fields, including online monitoring, augmented reality, and so on. On one hand, traditional methods, such as Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP), are struggling to balance measuring efficiency and accuracy. On the other hand, deep learning-based approaches, which offer the potential for improved accuracy, are hindered by large parameter amounts and complex structures less amenable to real-time requirements. To solve this problem, we proposed a network architecture search (NAS)-based method for real-time processing and 3D measurement of dynamic scenes with rate equivalent to single-shot. A NAS-optimized lightweight neural network was designed for efficient phase demodulation, while an improved dual-frequency strategy was employed coordinately for flexible absolute phase unwrapping. The experiment results demonstrate that our method can effectively perform 3D reconstruction with a reconstruction speed of 58fps, and realize high-accuracy measurement of dynamic scenes based on deep learning for what we believe to be the first time with the average RMS error of about 0.08 mm.
实现动态场景的实时高精度三维重建是包括在线监测、增强现实等众多领域面临的一项基本挑战。一方面，传统方法，如傅里叶变换轮廓术（FTP）和相移轮廓术（PSP），难以在测量效率和精度之间取得平衡。另一方面，基于深度学习的方法虽然有提高精度的潜力，但却受到大量参数和复杂结构的阻碍，不太适合实时需求。为了解决这个问题，我们提出了一种基于网络架构搜索（NAS）的方法，用于动态场景的实时处理和三维测量，其速率等同于单次拍摄。设计了一个经NAS优化的轻量级神经网络用于高效相位解调，同时协同采用改进的双频策略进行灵活的绝对相位展开。实验结果表明，我们的方法能够以58帧每秒的重建速度有效地进行三维重建，并首次基于深度学习实现动态场景的高精度测量，平均均方根误差约为0.08毫米。
