Untrained deep learning-based phase retrieval for fringe projection profilometry
Haotian Yu, Xiaoyu Chen, Ruobing Huang, Lianfa Bai, Dongliang Zheng, Jing Han
Abstract
Fringe projection profilometry (FPP) based on deep learning shows potential for challenging 3-D sensing tasks, e.g., bio-medicine, reverse engineering, and face recognition , etc. Supervised deep learning has been introduced to retrieve the desired phase for the 3-D reconstruction, which relies on lots of advanced training to construct the fringe-to-phase transformation. The traditional deep learning-based method becomes unreliable for scenes that are different from the training ones, which restricts it to be applied for actual applications. In this paper, an untrained deep learning-based phase retrieval method is proposed. By adding a camera to the traditional FPP system, scene-independent physical constraints such as phase, structure and 3-D consistency are constructed to optimize the fringe-to-phase transformation. The proposed deep learning-based method can retrieve the desired phase from a single fringe pattern without advance training. Both theoretical analyses and experimental results demonstrate its accurateness and robustness. The proposed method also shows potential for single-shot 3-D sensing applications such as high-speed 3-D measurement.