Graph Cut based Continuous Stereo Matching using Locally Shared Labels

Tatsunori Taniai, Yasuyuki Matsushita, Takeshi Naemura; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1613-1620

Abstract


We present an accurate and efficient stereo matching method using locally shared labels, a new labeling scheme that enables spatial propagation in MRF inference using graph cuts. They give each pixel and region a set of candidate disparity labels, which are randomly initialized, spatially propagated, and refined for continuous disparity estimation. We cast the selection and propagation of locallydefined disparity labels as fusion-based energy minimization. The joint use of graph cuts and locally shared labels has advantages over previous approaches based on fusion moves or belief propagation; it produces submodular moves deriving a subproblem optimality; enables powerful randomized search; helps to find good smooth, locally planar disparity maps, which are reasonable for natural scenes; allows parallel computation of both unary and pairwise costs. Our method is evaluated using the Middlebury stereo benchmark and achieves first place in sub-pixel accuracy.

Related Material


[pdf]
[bibtex]
@InProceedings{Taniai_2014_CVPR,
author = {Taniai, Tatsunori and Matsushita, Yasuyuki and Naemura, Takeshi},
title = {Graph Cut based Continuous Stereo Matching using Locally Shared Labels},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}