Stereo Matching With Fusing Adaptive Support Weights

Stereo matching has been widely used in various computer vision applications and it is still a challenging problem. Adaptive support weights (ASW) methods represent the state of the art in stereo matching and have achieved outstanding performance. However, the local ASW methods fail to resolve the matching ambiguity in low texture areas because their cost aggregation is limited within local fixed or adaptive support windows. On the other hand, the non-local ASW methods perform cost aggregation along a special tree, so that these methods are often sensitive to high texture areas since some useful connectivity constrains between adjacent pixels are broken during constructing the special tree. To solve these problems, in this paper, a novel and generic fusing ASW framework are proposed for stereo matching. In this framework, we establish dual support windows for each pixel, i.e., a local window and the whole image window. As such, the primitive connectivity between each pixel and its neighboring pixels in the local window can be maintained, and then each pixel not only gets appropriate supports from neighboring pixels within its local support window but also receives more adaptive supports from the other pixels outside the local window. Furthermore, a local edge-aware filter and a non-local edge-aware filter, whose kernel windows correspond to the dual support windows, are merged in order to achieve collaborative filtering of the cost volume. The performance evaluation on the Middlebury and KITTI datasets shows that the proposed stereo matching method outperforms the current state-of-the-art methods.