Patchmatch Stereo++: Patchmatch Binocular Stereo with Continuous Disparity Optimization

Current deep-learning-based stereo matching algorithms achieve remarkably low error rates but they suffer from the edge ambiguity effect. The primary reason is that they treat disparity estimation as a labeling problem, constructing a cost volume based on uniform discrete pixel-wise labels. It is insufficient to model the continuous disparity probability distribution (DPD), which harms the accuracy of complex regions. Moreover, current cost aggregation strategies cannot process unstructured disparity candidates very well, which is one of the bottlenecks limiting continuous modeling. We propose Patchmatch Stereo++, inspired by the traditional Patchmatch Stereo to achieve better continuous disparity optimization in deep-learning-based methods. Firstly, to model accurate continuous DPD, we introduce an adaptive dense sub-pixel sampling strategy to binocular stereo and approximate a continuous unstructured DPD for every pixel. Secondly, we design a convolution-based optimizer that can accept unstructured disparity candidates to parse the above continuous DPD in an adaptive manner and perform updates accordingly. Extensive experiments demonstrate our method has the best performance among existing stereo matching networks at the edges, both quantitatively and qualitatively. At the time of submission, compared with published works pre-trained on SceneFlow, we rank 1st in the foreground of KITTI and 2nd on SceneFlow, ETH3D under various metrics.The source code will be released.