Abstract
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.
Highlights
In many multiview stereo (MVS) schemes, the matching cost volume (MCV) is commonly employed to generate and find the optimal disparity space image
We introduced a novel multiview stereo matching method for estimating accurate disparity in widetextureless and occluded-textureless regions
In the cost volume-based stereo matching, refining the incorrect disparity either in wide-textureless or occluded-textureless region is difficult with only using matching cost computation
Summary
In many multiview stereo (MVS) schemes, the matching cost volume (MCV) is commonly employed to generate and find the optimal disparity (inverse depth) space image. The semiglobal matching (SGM) method [1], which is one of the famous stereo matching methods, employs the dynamic programming algorithm for the cost aggregation of the matching cost volume. The Soft3D method [2] employs the plane sweep stereo scheme [3] to generate multiview MCV and iteratively update the cost volumes using the visibility of a pixel from every view. In SGM, a dynamic programming algorithm is employed for depth continuity preservation and multidirectional cost summation for global cost optimization. The dynamic programming algorithm in SGM uses the disparity of the neighbor pixel in the smoothness-preserving path of the cost aggregation. The optimal depth is determined by the WinnerTake-All (WTA) manner
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