Shift Convolution Network for Stereo Matching
This addresses faster and more accurate stereo matching for computer vision applications, but it is incremental as it builds on existing network architectures.
The paper tackles stereo matching by introducing Shift Convolution Network (ShiftConvNet), which replaces traditional correlation with a shift convolution layer to speed up disparity map generation, achieving state-of-the-art results on FlyingThings 3D with 5 fps.
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.