Disparity refinement based on feature classification and local propagation for stereo matching

Hanqing Zhao; Yi Wan

doi:10.1117/12.2626551

20 December 2021 Disparity refinement based on feature classification and local propagation for stereo matching

Hanqing Zhao, Yi Wan

Proceedings Volume 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021); 1215509 (2021) https://doi.org/10.1117/12.2626551
Event: International Conference on Computer Vision, Application, and Design (CVAD 2021), 2021, Sanya, China

Abstract

Stereo matching usually makes up of four steps: cost computation, cost aggregation, disparity optimization, and disparity refinement. The disparity refinement is used to further eliminate mismatches caused by occlusion, low texture, and other factors. The popular refinement methods are based on the consistency check of left and right two disparity maps. For efficiency, we propose a novel multistep disparity refinement framework using only one-sided image, which is organized into four main steps: leftmost occlusion detection, four-directional scanline outlier detection, black hole detection and eight-directional disparity propagation. Experimental results on Middlebury datasets show that our method is comparable with other postprocessing strategies, especially in occlusion handling, retaining object shapes and preserving discontinuities.

Citation Download Citation

Hanqing Zhao and Yi Wan "Disparity refinement based on feature classification and local propagation for stereo matching", Proc. SPIE 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021), 1215509 (20 December 2021); https://doi.org/10.1117/12.2626551

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