  Learning-based stereo matching has recently achieved promising results, yet
still suffers difficulties in establishing reliable matches in weakly matchable
regions that are textureless, non-Lambertian, or occluded. In this paper, we
address this challenge by proposing a stereo matching network that considers
pixel-wise matchability. Specifically, the network jointly regresses disparity
and matchability maps from 3D probability volume through expectation and
entropy operations. Next, a learned attenuation is applied as the robust loss
function to alleviate the influence of weakly matchable pixels in the training.
Finally, a matchability-aware disparity refinement is introduced to improve the
depth inference in weakly matchable regions. The proposed deep stereo
matchability (DSM) framework can improve the matching result or accelerate the
computation while still guaranteeing the quality. Moreover, the DSM framework
is portable to many recent stereo networks. Extensive experiments are conducted
on Scene Flow and KITTI stereo datasets to demonstrate the effectiveness of the
proposed framework over the state-of-the-art learning-based stereo methods.
