Structured light 3D shape measurement for translucent media base on deep Bayesian inference

Traditional structured light technique faces challenges in measuring translucent media due to low fringe modulation and strong random noise caused by subsurface scattering, thereby significantly reducing phase quality. In addition, the difficulty in obtaining ground truth makes it hard to assess reliability even though obtaining measured results. Here, we proposed a 3D measurement method for translucent media base on deep Bayesian inference to achieve both fringe enhancement and phase uncertainty evaluation. Specifically, a deep network incorporated with quatuor-branch residual block is developed to significantly enhance the fringe modulation and signal-to-noise ratio (SNR) for accurate phase recovery. Subsequently, a Bayesian inference mechanism is established for probabilistic statistics, which allows for the optimization of fringe output and provides uncertainty self-evaluation based on Monte Carlo (MC) sampling. Furthermore, by incorporating both numerical and physical constraints into the supervised learning, the network can effectively mitigate phase-shifted errors in the final results. The proposed method shows high efficiency and flexibility since it requires no additional patterns or hardware setup. Experiments validate the feasibility of the proposed method.