  Dense stereo matching with deep neural networks is of great interest to the
research community. Existing stereo matching networks typically use slow and
computationally expensive 3D convolutions to improve the performance, which is
not friendly to real-world applications such as autonomous driving. In this
paper, we propose the Efficient Stereo Network (ESNet), which achieves high
performance and efficient inference at the same time. ESNet relies only on 2D
convolution and computes multi-scale cost volume efficiently using a
warping-based method to improve the performance in regions with fine-details.
In addition, we address the matching ambiguity issue in the occluded region by
proposing ESNet-M, a variant of ESNet that additionally estimates an occlusion
mask without supervision. We further improve the network performance by
proposing a new training scheme that includes dataset scheduling and
unsupervised pre-training. Compared with other low-cost dense stereo depth
estimation methods, our proposed approach achieves state-of-the-art performance
on the Scene Flow [1], DrivingStereo [2], and KITTI-2015 dataset [3]. Our code
will be made available.
