The KITTI Vision Benchmark Suite

Optical flow is an essential step for many
computer vision tasks. Existing deep networks have
achieved satisfactory results by mostly employing
a pyramidal coarse-to-fine paradigm, where a key
process is to adopt warped target feature based on
previous flow prediction to correlate with source
feature for building 3D matching cost volume.
However, the warping operation can lead to
troublesome ghosting problem that results in
ambiguity. Moreover, occluded areas are treated
equally with non occluded regions in most existing
works, which may cause performance degradation. To
deal with these challenges, we propose a
lightweight yet efficient optical flow network,
named OAS-Net for accurate optical flow. First, a
new sampling based correlation layer is employed
without noisy warping operation. Second, a novel
occlusion aware module is presented to make raw
cost volume conscious of occluded regions. Third,
a shared flow and occlusion awareness decoder is
adopted for structure compactness.

OAS-Net

@inproceedings{kong2021oasnet,
author={Lingtong Kong and Xiaohang Yang and Jie
Yang},
booktitle={IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP)},
title={OAS-Net: Occlusion Aware Sampling Network
for Accurate Optical Flow},
year={2021},}