论文标题
立体声匹配与基于成本量的稀疏差异传播
Stereo Matching with Cost Volume based Sparse Disparity Propagation
论文作者
论文摘要
立体声匹配对于双眼立体视觉至关重要。现有方法主要集中于简单的差异图融合以改善立体声匹配，这需要多个密集或稀疏的差异图。在本文中，我们提出了一个简单而新颖的方案，称为特征差异传播，以根据匹配的成本量和稀疏匹配特征点来改善一般立体声匹配。具体而言，我们的方案首先通过本地特征匹配来计算可靠的稀疏差异图，然后通过将可靠的差异传播到匹配的成本域中的相邻像素来完善差异图。此外，考虑到本地差异区域的梯度和多规模信息，我们根据众所周知的AD-Census提出了$ρ$ census的成本度量，即使没有成本汇总步骤，也可以保证成本量的稳健性。对Middlebury立体基准V3进行的广泛实验表明，我们的方案实现了与最新方法相当的有希望的性能。
Stereo matching is crucial for binocular stereo vision. Existing methods mainly focus on simple disparity map fusion to improve stereo matching, which require multiple dense or sparse disparity maps. In this paper, we propose a simple yet novel scheme, termed feature disparity propagation, to improve general stereo matching based on matching cost volume and sparse matching feature points. Specifically, our scheme first calculates a reliable sparse disparity map by local feature matching, and then refines the disparity map by propagating reliable disparities to neighboring pixels in the matching cost domain. In addition, considering the gradient and multi-scale information of local disparity regions, we present a $ρ$-Census cost measure based on the well-known AD-Census, which guarantees the robustness of cost volume even without the cost aggregation step. Extensive experiments on Middlebury stereo benchmark V3 demonstrate that our scheme achieves promising performance comparable to state-of-the-art methods.
