The KITTI Vision Benchmark Suite

Our goal is to significantly speed up the runtime of
current state-of-the-art stereo algorithms to enable
real-time inference. Towards this goal, we developed a
differentiable PatchMatch module that allows us to
discard most disparities without requiring full cost
volume evaluation. We then exploit this representation
to learn which range to prune for each pixel. By
progressively reducing the search space and
effectively propagating such information, we are able
to efficiently compute the cost volume for high
likelihood hypotheses and achieve savings in both
memory and computation. Finally, an image guided
refinement module is exploited to further improve the
performance. Since all our components are
differentiable, the full network can be trained end-to-
end. Our experiments show that our method achieves
competitive results on KITTI and SceneFlow datasets
while running in real-time at 62ms.

Please refer to the paper:
https://arxiv.org/abs/1909.05845

@inproceedings{Duggal2019ICCV,
title = {DeepPruner: Learning Efficient Stereo Matching
via Differentiable PatchMatch},
author = {Shivam Duggal and Shenlong Wang and Wei-
Chiu Ma and Rui Hu and Raquel Urtasun},
booktitle = {ICCV},
year = {2019}}