The KITTI Vision Benchmark Suite

Stereo matching is one of the most important and
fundamental topics in computer vision. Encouraging
self-similar
pixels to be assigned to the same label has been
proved to be effective for stereo. A typical way
of taking advantage
of self-similarity is performing a color
segmentation on the image and motivating the
pixels within each segment
to share an identical label. However, some cases
cannot be handled by image segmentation, such as
the pixels in
disconnected regions. This paper proposes a stereo
method based on the assumption, that a 3D scene is
a collection
of a few smooth surfaces and a few classes of
reflective materials, such that the 3D points
belonging to an identical
material are likely to lie on a small number of
surfaces and the 3D points lying on a single
surface belong to a few
classes of reflective materials. Each material is
expected to have specific albedo properties. This
paper presents two
methods for classifying the albedo properties
depending on whether the illumination environment
is known, without
recovering the albedo parameters. The proposed
model is formulated as an energy function
incorporating some new
priors, that is optimized via fusion move
algorithm.

comming soon

@article{yang2016stereo,
title={Stereo matching based on classification of
materials},
author={Yang, Menglong and Liu, Yiguang and Cai,
Ying and You, Zhisheng},
journal={Neurocomputing},
volume={194},
pages={308--316},
year={2016}}