Adaptive stereo matching via loop-erased random walk
Adaptive stereo matching via loop-erased random walk
复制标题
通过循环擦除随机游走的自适应立体匹配
DOI：
10.1109/icip.2014.7025769
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Hongtao Lu
中科院分区：
文献类型：
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作者：
Xuejiao Bai;Xuan Luo;S. Li;Hongtao Lu
提出了一种基于自适应树的立体匹配成本聚合策略。以往基于树的算法受最小生成树（MST）贪婪性的限制，提供的自适应支持窗口较差，在曲面和斜面上的性能较差。该方法结合了随机性，并通过在树构造中引入循环擦除随机行走（LERW）来克服这些缺点。在Middlebury数据集上的实验结果表明，在大多数高分辨率测试用例中，我们基于lerw的策略优于其他基于树的最先进策略。我们的贡献包括：1)基于低成本成本的成本聚合策略；2)基于lerw的细化方法；3)对我国支撑窗的适应性进行数学分析。
This paper proposes an adaptive tree-based cost aggregation strategy for stereo matching. The previous tree-based algorithms, hindered by the greediness of minimum spanning tree (MST), provide poorly adaptive support windows and have bad performance on curved and slanted surfaces. The proposed method incorporates randomness and overcomes these drawbacks by introducing loop-erased random walk (LERW) into tree construction. Experimental results over Middlebury dataset demonstrate that our LERW-based strategy outperforms other tree-based state-of-the-art strategies in most of the high resolution test cases. Our contributions include: 1) an LERW-based cost aggregation strategy; 2) an LERW-based refinement method; 3) mathematical analysis of the adaptability of our support windows.