The KITTI Vision Benchmark Suite

We present a new deep learning-based approach for
dense stereo matching. Compared to previous works, our
approach does not use deep learning of pixel appearance
descriptors, employing very fast classical matching
scores instead. At the same time, our approach uses a
deep convolutional network to predict the local
parameters of cost volume aggregation process, which in
this paper we implement using differentiable domain
transform. By treating such transform as a recurrent
neural network, we are able to train our whole system that
includes cost volume computation, cost-volume
aggregation (smoothing), and winner-takes-all disparity
selection end-to-end. The resulting method is highly
efficient at test time while achieving good matching
accuracy. On the KITTI 2015 benchmark, it achieves a
result of 6.34\% error rate while running at 29 frames per
second rate on a modern GPU.

SAD 1x1, CENSUS 7x7
\alpha = 0.43
\sigma = 4

@inproceedings{kuzmin2017end,
title={End-to-end Learning of Cost-Volume Aggregation
for
Real-time Dense Stereo},
author={Kuzmin, Andrey and Mikushin, Dmitry and
Lempitsky,
Victor},
booktitle = {2017 IEEE 27th International Workshop on
Machine Learning for Signal Processing (MLSP)},
year={2017}
}