  Existing state-of-the-art disparity estimation works mostly leverage the 4D
concatenation volume and construct a very deep 3D convolution neural network
(CNN) for disparity regression, which is inefficient due to the high memory
consumption and slow inference speed. In this paper, we propose a network named
EDNet for efficient disparity estimation. Firstly, we construct a combined
volume which incorporates contextual information from the squeezed
concatenation volume and feature similarity measurement from the correlation
volume. The combined volume can be next aggregated by 2D convolutions which are
faster and require less memory than 3D convolutions. Secondly, we propose an
attention-based spatial residual module to generate attention-aware residual
features. The attention mechanism is applied to provide intuitive spatial
evidence about inaccurate regions with the help of error maps at multiple
scales and thus improve the residual learning efficiency. Extensive experiments
on the Scene Flow and KITTI datasets show that EDNet outperforms the previous
3D CNN based works and achieves state-of-the-art performance with significantly
faster speed and less memory consumption.
