The KITTI Vision Benchmark Suite

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.

See paper.

@inproceedings{kendall2017end,
title={End-to-End Learning of Geometry and Context for
Deep Stereo Regression},
author={Kendall, Alex and Martirosyan, Hayk and
Dasgupta, Saumitro and Henry, Peter and Kennedy, Ryan
and Bachrach, Abraham and Bry, Adam},
booktitle = {Proceedings of the International Conference on
Computer Vision ({ICCV})},
year={2017}
}