The KITTI Vision Benchmark Suite

Initializing optical flow field by either sparse
descriptor matching or dense patch matches has been
proved to be particularly useful for capturing large
displacements. In this paper, we present a pyramidal
gradient matching approach that can provide dense
matches for highly accurate and efficient optical
flow estimation. A novel contribution of our method
is that image gradient is used to describe image
patches and proved to be able to produce robust
matching. Therefore, our method is more efficient
than methods that adopt special features (like SIFT)
or patch distance metric. Moreover, we find that
image gradient is scalable for optical flow
estimation, which means we can use different levels
of gradient feature (for example, full gradients or
only direction information of gradients) to obtain
different complexity without dramatic changes in
accuracy. Another contribution is that we uncover
the secrets of limited PatchMatch through a thorough
analysis and design a pyramidal matching framework
based these secrets. Our pyramidal matching
framework is aimed at robust gradient matching and
effective to grow inliers and reject outliers. In
this framework, we present some special enhancements
for outlier filtering in gradient matching. By
initializing EpicFlow with our matches, experimental
results show that our method is efficient and robust
(ranking 1st on both clean pass and final pass of
MPI Sintel dataset among published methods).

...

@article{DBLP:journals/corr/Li17n,
author = {Yuanwei Li},
title = {Pyramidal Gradient Matching for
Optical Flow Estimation},
journal = {CoRR},
volume = {abs/1704.03217},
year = {2017},
url = {http://arxiv.org/abs/1704.03217},
archivePrefix = {arXiv},
eprint = {1704.03217},
timestamp = {Wed, 07 Jun 2017 14:40:02 +0200},
biburl =
{http://dblp.org/rec/bib/journals/corr/Li17n},
bibsource = {dblp computer science bibliography,
http://dblp.org}
}