The KITTI Vision Benchmark Suite

Optical Flow (OF) and depth are commonly used for
visual odometry since they provide sufficient
information about camera ego-motion in a rigid
scene. We reformulate the problem of ego-motion
estimation as a problem of motion estimation of a
3D-scene with respect to a static camera. The
entire scene motion can be represented as a
combination of motions of its visible points.
Using OF and depth we estimate a motion of each
point in terms of 6DoF and represent results in
the form of motion maps, each one addressing
single degree of freedom. In this work we provide
motion maps as inputs to a deep neural network
that predicts 6DoF of scene motion. Through our
evaluation on outdoor and indoor datasets we show
that utilizing motion maps leads to accuracy
improvement in comparison with naive stacking of
depth and OF. Another contribution of our work is
a novel network architecture that efficiently
exploits motion maps and outperforms learnable
RGB/RGB-D baselines.

supervised=True

@misc{slinko2019scene,
title={Scene Motion Decomposition for
Learnable Visual Odometry},
author={Igor Slinko and Anna Vorontsova and
Filipp Konokhov and Olga Barinova and Anton
Konushin},
year={2019},
eprint={1907.07227},
archivePrefix={arXiv},
primaryClass={cs.CV}
}