The KITTI Vision Benchmark Suite

Image segments are often used as a constraint
in stereo matching. However, both over-
segmentation and under-segmentation can lead to
disparity degradation in some regions. To
obtain an accurate disparity map, we introduce
the notion of object and build a new global
energy function to optimize segments and
estimate a disparity map jointly. The effective
and efficient block coordinate descent approach
is used to optimize the global energy function
by merging small segments. We demonstrate the
performance of the proposed algorithm on KITTI
benchmarks. The results show that our algorithm
outperforms many state-of-the-art methods and
confirm the effectiveness of approach.

n=2000, λ_color=20, λ_disparity=10, λ_bound=1,
λ_outlier=10, λ_total=30, λ_smooth=5,
λ_occlu=12, λ_hinge=4, λ_copla=1.

@INPROCEEDINGS{Xuesong2016ICIP,
author = {Xuesong Li and Jianguo Liu},
title = {EFFICIENT STEREO MATCHING USING SEGMENT
OPTIMIZATION},
booktitle= {ICIP },
year = {2016}
}