The KITTI Vision Benchmark Suite

We present a neural network architecture applied to
the
problem of refining a dense disparity map generated
by
a stereo algorithm to which we have no access. Our
approach is able to learn which disparity values
should be
modified and how, from a training set of images,
estimated
disparity maps and the corresponding ground truth.
Its only
input at test time is a disparity map and the
reference image. Two design characteristics are
critical for the success
of our network: (i) it is formulated as a recurrent
neural
network, and (ii) it estimates the output refined
disparity
map as a combination of residuals computed at
multiple
scales, that is at different up-sampling and down-
sampling
rates. The first property allows the network, which
we named
RecResNet, to progressively improve the disparity
map, while
the second property allows the corrections to come
from
different scales of analysis, addressing different
types of
errors in the current disparity map. We present
competitive quantitative and qualitative results on
the KITTI 2012
and 2015 benchmarks that surpass the accuracy of
previous disparity refinement methods.

See our paper

@inproceedings{batsos2018recresnet,
title={RecResNet: A Recurrent Residual CNN
Architecture for Disparity Map Enhancement},
author={Batsos, Konstantinos and Mordohai,
Philipos},
booktitle={ In International Conference on 3D
Vision (3DV) },
year={2018}
}