End-to-end color fringe depth estimation based on a three-branch U-net network

In fringe projection profilometry (FPP), end-to-end depth estimation from fringe patterns for FPP attracts more and more attention from fringe patterns. However, color images provide additional information from the RGB channel for FPP, which has been paid little attention in depth estimation. To this end, in this paper we present for the first time, to the best of our knowledge, an end-to-end network for depth estimation using color composite fringes with better performance. In order to take advantage of the color fringe pattern, a multi-branch structure is designed in this paper, which learns the multi-channel details of the object under test by using three encoders for each RGB channel and introduces an attention module to better capture the complex features and modalities information in the input data. Experiments from simulated and real datasets show that the proposed method with color fringe pattern is effective for depth estimation, and it outperforms other deep learning methods such as UNet, R2Unet, PCTNet, and DNCNN.