Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.

中文翻译：

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使用基于正交权重的基于自适应跨区域的导引图像滤波进行局部立体匹配

基于自适应跨区域的导引图像滤波（ACR-GIF）是一种常用的成本汇总方法。但是，通常不会考虑自适应跨区域（ACR）中点的权重，这会影响视差结果的准确性。在这项研究中，我们提出了一种改进的成本汇总方法来解决此问题。首先，根据ACR的结构特征提出正交权重，然后计算ACR中每个点的正交权重。其次，使用具有正交权重（ACR-GIF-OW）的ACR-GIF过滤匹配的成本量。为了减少该方法的计算时间，提出了一种基于正交加权的高效加权聚合计算方法。此外，通过将ACR-GIF-OW与我们最近提出的匹配成本计算方法和视差细化方法相结合，还提出了一种局部立体声匹配算法。Middlebury评估平台的结果表明，与ACR-GIF相比，所提出的成本汇总方法可以显着提高视差准确性，同时减少了额外的时间开销，并且所提出的立体声匹配算法的性能优于其他最新技术。本地和非本地算法。