This study reinvestigated one of the most fundamental problems in structure light depth sensing field: correspondence retrieval of features between patterns and images. We formulate the global optimum correspondence retrieval by maximizing a conditional probability of correspondence given observed features, which is depicted by a Bayesian network. Different from traditional “code-only” based correspondence retrieval methods, the proposed Bayesian network based method exploits the positional correlations of correspondences of neighboring features, namely, the correspondences of poorly detected features are estimated with the aid of the correspondences of well detected features. The method performs especially well on challenging scenes with rich depth variations, abrupt depth changes, edges, etc. Experiments show that the proposed method increase the correspondence accuracy by about 40% on challenging scenes, compared with traditional “code-only” based correspondence retrieval methods.

这项研究重新研究了结构光深度传感领域中最基本的问题之一：图案和图像之间特征的对应检索。我们通过给定观测特征的对应条件的概率最大化来制定全局最优对应检索，这由贝叶斯网络描述。与传统的基于“仅代码”的对应关系检索方法不同，所提出的基于贝叶斯网络的方法利用了相邻特征的对应关系的位置相关性，即，借助检测良好的特征的对应关系来估计检测不良的特征的对应关系。该方法在深度变化丰富，深度变化突然，边缘等具有挑战性的场景中表现尤其出色。