DMCA
Efficient Large-Scale Stereo Matching
Cached
Download Links
| Citations: | 70 - 8 self |
BibTeX
@MISC{Geiger_efficientlarge-scale,
author = {Andreas Geiger and Martin Roser and Raquel Urtasun},
title = {Efficient Large-Scale Stereo Matching},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract. In this paper we propose a novel approach to binocular stereo for fast matching of high-resolution images. Our approach builds a prior on the disparities by forming a triangulation on a set of support points which can be robustly matched, reducing the matching ambiguities of the remaining points. This allows for efficient exploitation of the disparity search space, yielding accurate dense reconstruction without the need for global optimization. Moreover, our method automatically determines the disparity range and can be easily parallelized. We demonstrate the effectiveness of our approach on the large-scale Middlebury benchmark, and show that state-of-the-art performance can be achieved with significant speedups. Computing the left and right disparity maps for a one Megapixel image pair takes about one second on a single CPU core. 1
Keyphrases
efficient large-scale stereo matching accurate dense reconstruction fast matching right disparity map high-resolution image support point state-of-the-art performance significant speedup disparity search space global optimization single cpu core efficient exploitation novel approach disparity range megapixel image pair large-scale middlebury benchmark
