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Hybrid cost aggregation for dense stereo matching

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Abstract

Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions.

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, 78712, USA

    Ming Yao & Bugao Xu

  2. Department of Computer Science and Engineering, University of North Texas, Denton, TX, 76207, USA

    Wenbin Ouyang & Bugao Xu

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  1. Ming Yao
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  2. Wenbin Ouyang
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  3. Bugao Xu
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Correspondence to Bugao Xu.

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Yao, M., Ouyang, W. & Xu, B. Hybrid cost aggregation for dense stereo matching. Multimed Tools Appl 79, 23189–23202 (2020). https://doi.org/10.1007/s11042-020-09127-7

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  • DOI: https://doi.org/10.1007/s11042-020-09127-7

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