Stereo matching with space-constrained cost aggregation and segmentation-based disparity refinement
Stereo matching with space-constrained cost aggregation and segmentation-based disparity refinement
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DOI：
10.1117/12.2083741
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
Yi-Jia Peng;Ge Li;Ronggang Wang;Wenmin Wang
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文献类型：
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作者：
Yi-Jia Peng;Ge Li;Ronggang Wang;Wenmin Wang
立体匹配是计算机视觉中的一个基本课题。通常，立体匹配主要由四个阶段组成：代价计算、代价聚合、视差优化和视差细化。在本文中，我们提出了一种新的立体匹配方法与空间受限的成本聚合和基于分割的视差细化。最先进的方法用于成本聚合和差异优化阶段。本文给出了三个技术贡献。首先，在代价聚合阶段应用空间约束交叉区域;其次，在图像分割中利用颜色和视差信息;第三，使用图像分割和遮挡区域检测来辅助视差细化。我们平台的性能在Middlebury评估中排名第二。
Stereo matching is a fundamental topic in computer vision. Usually, stereo matching is mainly composed of four stages: cost computation, cost aggregation, disparity optimization and disparity refinement. In this paper, we propose a novel stereo matching method with space-constrained cost aggregation and segmentation-based disparity refinement. Stateof- the-art methods are used for cost aggregation and disparity optimization stages. Three technical contributions are given in this paper. First, applying space-constrained cross-region in cost aggregation stage; second, utilizing both color and disparity information in image segmentation; third, using image segmentation and occlusion region detection to aid disparity refinement. The performance of our platform ranks second in the Middlebury evaluation.