Title: End-to-End Learning of Geometry and Context for Deep Stereo Regression,
Abstract: We propose a novel deep learning architecture for regressing disparity from a
rectified pair of stereo images. We leverage knowledge of the problem's
geometry to form a cost volume using deep feature representations. We learn to
incorporate contextual information using 3-D convolutions over this volume.
Disparity values are regressed from the cost volume using a proposed
differentiable soft argmin operation, which allows us to train our method
end-to-end to sub-pixel accuracy without any additional post-processing or
regularization. We evaluate our method on the Scene Flow and KITTI datasets and
on KITTI we set a new state-of-the-art benchmark, while being significantly
faster than competing approaches.