  In this paper, we present Shift Convolution Network (ShiftConvNet) to provide
matching capability between two feature maps for stereo estimation. The
proposed method can speedily produce a highly accurate disparity map from
stereo images. A module called shift convolution layer is proposed to replace
the traditional correlation layer to perform patch comparisons between two
feature maps. By using a novel architecture of convolutional network to learn
the matching process, ShiftConvNet can produce better results than
DispNet-C[1], also running faster with 5 fps. Moreover, with a proposed auto
shift convolution refine part, further improvement is obtained. The proposed
approach was evaluated on FlyingThings 3D. It achieves state-of-the-art results
on the benchmark dataset. Codes will be made available at github.
