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CRAR: Accelerating Stereo Matching with Cascaded Residual Regression and Adaptive Refinement

Dense stereo matching estimates the depth for each pixel of the referenced images. Recently, deep learning algorithms have dramatically promoted the development of stereo matching. The state-of-the-art result is achieved by models adopting deep convolutional neural networks. However, a considerable computational burden is also introduced, which slows the inference. To solve this problem, previous works down-sampled the input images to decrease the spatial size. However, down-sampling increases the error rate and its lower bound. In this article, we accelerate stereo matching algorithms through the improvement of network structure. Inspired by network compression, we conduct decomposition and sparsification to squeeze the computationally expensive cost optimization network. It is sparsified and then decomposed into smaller networks, which are designed and trained in a cascaded manner to reach the nearest possible performance of the larger network. Previous methods have utilized numerous refinement methods to adjust the coarse disparity. We integrate refinement methods to create an unified algorithm to utilize parallelism for running devices to further accelerate the inference. The extensive experiments on Kitti2015, Kitti2012, and Middlebury datasets demonstrate the efficiency of our method.