Abstract
This paper presents a novel aggregation window method for stereo matching, by combining the disparity hypothesis costs of multiple pixels in a local region more efficiently for increased hypothesis confidence. We propose two adaptive windows per pixel region, one following the horizontal edges in the image, the other the vertical edges. Their combination defines the final aggregation window shape that rigorously follows all object edges, yielding better disparity estimations with at least 0.5 dB gain over similar methods in literature, especially around occluded areas. Also, a qualitative improvement is observed with smooth disparity maps, respecting sharp object edges. Finally, these shape-adaptive aggregation windows are represented by a single quadruple per pixel, thus supporting an efficient GPU implementation with negligible overhead.
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