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Confidence Map Based 3D Cost Aggregation with Multiple Minimum Spanning Trees for Stereo Matching

机译:基于置信度图的3D成本聚合和多个最小生成树用于立体匹配

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摘要

Stereo matching is a challenging problem due to the mismatches caused by difficult environment conditions. In this paper, we propose an enhanced version of our previous work, denoted as 3DMST-CM, to handle challenging cases and obtain a high-accuracy disparity map based on the ambiguity of image pixels. We develop a module of distinctiveness analysis to classify pixels into distinctive and ambiguous pixels. Then distinctive pixels are utilized as anchor pixels to help match ambiguous pixels accurately. The experimental results demonstrate the effectiveness of our method and reach state-of-the-art on the Middlebury 3.0 benchmark.
机译:由于困难的环境条件导致的不匹配,立体声匹配是一个具有挑战性的问题。在本文中,我们提出了以前工作的增强版本,称为3DMST-CM,以处理挑战性情况并基于图像像素的歧义获得高精度视差图。我们开发了独特性分析模块,将像素分为独特和模糊的像素。然后,将独特的像素用作锚点像素,以帮助准确匹配模糊的像素。实验结果证明了我们方法的有效性,并在Middlebury 3.0基准上达到了最新水平。

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