The KITTI Vision Benchmark Suite

Perform NNF with coarse-to-fine scheme to improve
coherency.

Filter matches with simple saliency as well as
forward–backward constant.

Locally affine estimation with RANSAC to increase the
robustness of interpolation.

[PatchMatchCtF]
verbose=0
limitHoriz=0
useDAISY=1
gridSize=3
borderSize=5
maxLevelIteration=9
downFactor=0.5f
sigma=0.8f
minSize=30
maxDisplacement=160.0f
stopItRatio=0.0001f
fbThreshold=3

[EdgeAwareInterpolator]
verbose=0
saliencyFilter=1
preFilter=1
useCity=1
useLA=1
useRanSaC=1
useVarRefine=1
saliencyTh=0.06f
preNNK=32
preTh=5.0f
nnK=160
euc=0.001f
kappa=-1.0f
ransacIteration=5
dtIteration=1
regularCoef=0.01f

[VariationalRefinement]
verbose=0
variationalIteration=5
fixedPointIteration=1
sorIteration=15
omega=1.6f
alpha=1.0f
beta=-1.0f
delta=0.2f
gamma=0.6f
kappa=-1.0f
zeta=0.1f
epsilon=0.001f

@article{FAN20181,
title = "Hierarchical coherency sensitive hashing and interpolation with
RANSAC for large displacement optical flow",
journal = "Computer Vision and Image Understanding",
volume = "175",
pages = "1 - 10",
year = "2018",
issn = "1077-3142",
doi = "https://doi.org/10.1016/j.cviu.2018.10.005",
url =
"http://www.sciencedirect.com/science/article/pii/S1077314218304223",
author = "Jingzhe Fan and Yan Wang and Lei Guo",
keywords = "Nearest neighbor field, Sparse to dense interpolation,
Optical flow",
abstract = "Nearest Neighbor Field (NNF) has shown excellent performance
for large displacement optical flow estimation recently. However, it
contains much noise and lacks of global constraint. In this paper we
present an effective approach, named HCSH (Hierarchical Coherency
Sensitive Hashing), which combines the coarse-to-fine scheme and random
search strategy, to enable NNF to enjoy the inherent smooth of coarse-
to-fine framework. Then besides the forward–backward check for NNF, we
also use auto-correlation to remove the unreliable correspondences in
flat regions, where the NNF is often considered ambiguous and the motion
can be naturally recovered by the latter interpolation. Inspired by
EpicFlow, we propose edge-aware interpolation (EAI-Flow) to filling the
gaps by removing correspondence. RANSAC is introduced to improve the
locality-weighted affine transformation estimation, with neighbor
propagation of affine model to reduce required iterations and speed up
the computation. Experimental validation shows that our approach
outperforms the state-of-the-art with more accurate optical flow."
}