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In the multiview stereo (MVS) vision, it is difficult to estimate accurate depth in the textureless and occluded regions. To solve this problem, several MVS investigations employ the matching cost volume (MCV) approach to refine the cost in the textureless and occluded regions. Usually, the matching costs in the large textureless image regions are not reliable. In addition, if an occluded region is also textureless, the matching cost contains a significant error. The goal of the proposed MVS method is to reconstruct accurate depth maps in both large-textureless and occluded-textureless regions by iteratively updating the erroneous disparity. The erroneous disparity in the textureless region is updated by the 3D disparity plane of the region in the inverse-depth space. Then, the surface consensus is computed and used to run the two processes, the surface consensus refinement and the matching cost update. By the iterative update of the 3D inverse depth plane, surface consensus, and matching cost, the performance of the depth reconstruction in the large-textureless and occluded-textureless regions is greatly improved. The performance of the proposed method is analyzed using the Middlebury multiview stereo dataset. The depth reconstruction performance is also compared with several stereo vision methods.

Refinement of Inverse Depth Plane in Textureless and Occluded Regions in a Multiview Stereo Matching Scheme