Open AccessMaximum a posteriori-based depth sensing with a single-shot maze pattern
Author(s) -
Ruodai Li,
Li Fu,
Yi Niu,
Guangming Shi,
Lili Yang,
Xiaodong Xie
Publication year - 2017
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.25.025332
Subject(s) - maximum a posteriori estimation , robustness (evolution) , computer science , artificial intelligence , a priori and a posteriori , prior probability , single shot , pattern recognition (psychology) , rgb color model , maximum likelihood , one shot , computer vision , monochromatic color , mathematics , optics , statistics , physics , bayesian probability , mechanical engineering , biochemistry , chemistry , philosophy , epistemology , gene , engineering
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.