Maximum a posteriori-based depth sensing with a single-shot maze pattern.

Abstract

This study addressed the general problem of correspondence retrieval for single-shot depth sensing where the coded features cannot be detected perfectly. The traditional correspondence retrieval technique can be regarded as maximum likelihood estimation with a uniform distribution prior assumption, which may lead to mismatches for two types of insignificant features: 1) incomplete features that cannot be detected completely because of edges, tiny objects, and many depth variations, etc.; and 2) distorted features disturbed by environmental noise. To overcome the drawback of the uniform distribution assumption, we propose a maximum a posteriori estimation-based correspondence retrieval method that uses the significant features as priors to estimate the weak or missing features. We also propose a novel monochromatic maze-like pattern, which is more robust to ambient illumination and the colors in scenes than the traditional patterns. Our experimental results demonstrate that the proposed system performs better than the popular RGB-D cameras and traditional single-shot techniques in terms of accuracy and robustness, especially with challenging scenes.