Tan Chunqian, Song Wanzhong
College of Computer Science, Sichuan University, Chengdu 610065, China.
Sensors (Basel). 2024 Mar 6;24(5):1701. doi: 10.3390/s24051701.
Fringe projection profilometry (FPP) is widely used for high-accuracy 3D imaging. However, employing multiple sets of fringe patterns ensures 3D reconstruction accuracy while inevitably constraining the measurement speed. Conventional dual-frequency FPP reduces the number of fringe patterns for one reconstruction to six or fewer, but the highest period-number of fringe patterns generally is limited because of phase errors. Deep learning makes depth estimation from fringe images possible. Inspired by unsupervised monocular depth estimation, this paper proposes a novel, weakly supervised method of depth estimation for single-camera FPP. The trained network can estimate the depth from three frames of 64-period fringe images. The proposed method is more efficient in terms of fringe pattern efficiency by at least 50% compared to conventional FPP. The experimental results show that the method achieves competitive accuracy compared to the supervised method and is significantly superior to the conventional dual-frequency methods.
条纹投影轮廓术（FPP）被广泛用于高精度三维成像。然而，采用多组条纹图案可确保三维重建精度，但不可避免地会限制测量速度。传统的双频FPP将一次重建所需的条纹图案数量减少到六个或更少，但由于相位误差，条纹图案的最高周期数通常受到限制。深度学习使从条纹图像进行深度估计成为可能。受无监督单目深度估计的启发，本文提出了一种用于单相机FPP的新型弱监督深度估计方法。经过训练的网络可以从三帧64周期条纹图像中估计深度。与传统FPP相比，该方法在条纹图案效率方面至少提高了50%。实验结果表明，该方法与有监督方法相比具有相当的精度，并且明显优于传统的双频方法。
