Traditional semi-global matching (SGM) lacks interaction between scanlines and struggles to deal with the ambiguity of pixels in homogenous areas. In this paper, we propose a novel path-centering graph to perform weighted omnidirectional SGM (WOdSGM), in which the input image is divided into eight sub-trees, corresponding to eight optimization directions. In each pass, the outputs of pixels are recursively computed from leaf nodes to the root node along the tree structure. As the results of SGM from multiple scanlines often show different biases owing to respective message propagation directions, one solution is accurate in certain areas while others are not. By delving into the distribution of aggregated costs for pixels from various structures, we find that the minimum cost of reliable output is heavily biased from others. Therefore, we perform line fitting to approximate the distribution of aggregated costs and use the distance between the fitted line and the point corresponding to the minimum cost to evaluate the reliability of aggregated costs. Furthermore, we present a weighted fusion strategy to incorporate outputs from multiple directions, enabling our method to provide directionally biased constraints. Extensive experiments and analyses on widely used stereo datasets show that our approach outperforms typical traditional SGM-based algorithms.

中文翻译：

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加权全向半全局立体匹配

传统的半全局匹配（SGM）缺乏扫描线之间的交互，并且难以处理同质区域中像素的模糊性。在本文中，我们提出了一种新颖的路径中心图来执行加权全向SGM（WOdSGM），其中输入图像被分为八个子树，对应于八个优化方向。在每次传递中，沿着树结构从叶节点到根节点递归地计算像素的输出。由于来自多个扫描线的 SGM 结果常常因各自的消息传播方向而表现出不同的偏差，因此一种解决方案在某些区域是准确的，而其他解决方案则不然。通过深入研究各种结构的像素的聚合成本的分布，我们发现可靠输出的最小成本与其他成本有很大偏差。因此，我们进行直线拟合来近似汇总成本的分布，并使用拟合线与最小成本对应的点之间的距离来评估汇总成本的可靠性。此外，我们提出了一种加权融合策略来合并来自多个方向的输出，使我们的方法能够提供方向偏置约束。对广泛使用的立体数据集进行的大量实验和分析表明，我们的方法优于典型的传统基于 SGM 的算法。