Cross-Scale Cost Aggregation for Stereo Matching
Cross-Scale Cost Aggregation for Stereo Matching
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DOI：
10.1109/tcsvt.2015.2513663
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发表时间：
2017-05
影响因子：
8.4
通讯作者：
Kang Zhang;Yuqiang Fang;Dongbo Min;Lifeng Sun;Shiqiang Yang;Shuicheng Yan
中科院分区：
文献类型：
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作者：
Kang Zhang;Yuqiang Fang;Dongbo Min;Lifeng Sun;Shiqiang Yang;Shuicheng Yan
本文提出了一种通用框架，使立体匹配算法的代价聚合步骤能够实现多尺度交互。受图像滤波器公式的启发，我们首先从加权最小二乘（WLS）优化的角度重新定义了代价聚合，并表明不同的代价聚合方法在相似核的选择上存在本质差异。我们的主要动机是，虽然人类立体视觉系统在通信搜索中交互处理粗尺度和细尺度的信息，但最先进的方法仅在输入立体图像的最细尺度上累积成本，忽略了多个尺度之间的相互一致性。这一动机促使我们在WLS优化目标中引入尺度间正则化器，以加强相邻尺度间成本体积的一致性。新的尺度间正则化优化目标是凸的，因此易于解析求解。最小化这个新目标导致了所提议的框架。由于正则化项独立于相似核，各种代价聚合方法，包括离散和连续参数化方法，可以很容易地集成到所提出的框架中。在Middlebury、Middlebury Third、KITTI和New Tsukuba数据集上进行评估时，我们发现跨尺度框架很重要，因为它有效地扩展了最先进的成本汇总方法，并带来了重大改进。
This paper proposes a generic framework that enables a multiscale interaction in the cost aggregation step of stereo matching algorithms. Inspired by the formulation of image filters, we first reformulate cost aggregation from a weighted least-squares (WLS) optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Our key motivation is that while the human stereo vision system processes information at both coarse and fine scales interactively for the correspondence search, state-of-the-art approaches aggregate costs at the finest scale of the input stereo images only, ignoring inter-consistency across multiple scales. This motivation leads us to introduce an inter-scale regularizer into the WLS optimization objective to enforce the consistency of the cost volume among the neighboring scales. The new optimization objective with the inter-scale regularization is convex, and thus, it is easily and analytically solved. Minimizing this new objective leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation approaches, including discrete and continuous parameterization methods, can be easily integrated into the proposed framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, Middlebury Third, KITTI, and New Tsukuba data sets.