Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various applications. The major challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded, and discontinuous regions. In this paper, the proposed stereo matching system uses segment-based superpixels and matching cost. After determination of edge and smooth regions and selection of matching cost, we suggest the segment-based adaptive support weights in cost aggregation instead of color similarity and spatial proximity only. The proposed dual-path depth refinements use the cross-based support region by referring texture features to correct the inaccurate disparities with iterative procedures to improve the depth maps for shape reserving. Specially for leftmost and rightmost regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results show that the proposed system can achieve higher accurate depth maps than the conventional methods.

中文翻译：

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保留形状的立体声匹配以及基于分段的成本汇总和双路径优​​化

立体匹配是计算机视觉中最重要的主题之一，旨在为各种应用生成精确的深度图。立体声匹配的主要挑战是抑制在平滑，遮挡和不连续区域发生的不可避免的错误。在本文中，提出的立体匹配系统使用基于片段的超像素和匹配成本。在确定边缘和平滑区域并选择匹配成本之后，我们建议在成本汇总中使用基于分段的自适应支持权重，而不仅仅是颜色相似度和空间接近度。所提出的双路径深度细化通过参考纹理特征来使用基于交叉的支持区域，以通过迭代过程来校正不精确的视差，从而改善了用于形状保留的深度图。特别针对最左边和最右边的区域，基于片段的细化可以大大改善不匹配的视差孔。实验结果表明，与常规方法相比，该系统可以实现更高的精度深度图。