The KITTI Vision Benchmark Suite

Feature warping is a core technique in optical
flow estimation; however, the ambiguity caused by
occluded areas during warping is a major problem
that remains unsolved. In this paper, we propose
an asymmetric occlusion-aware feature matching
module, which can learn a rough occlusion mask
that filters useless (occluded) areas immediately
after feature warping without any explicit
supervision. The proposed module can be easily
integrated into end-to-end network architectures
and enjoys performance gains while introducing
negligible computational cost. The learned
occlusion mask can be further fed into a
subsequent network cascade with dual feature
pyramids with which we achieve state-of-the-art
performance. At the time of submission, our
method, called MaskFlownet, surpasses all
published optical flow methods on the MPI Sintel,
KITTI 2012 and 2015 benchmarks.

CTSKCTSK

@inproceedings{zhao2020maskflownet,
author = {Zhao, Shengyu and Sheng, Yilun and Dong,
Yue and Chang, Eric I-Chao and Xu, Yan},
title = {MaskFlownet: Asymmetric Feature Matching
with Learnable Occlusion Mask},
booktitle = {Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}