Efficient Large-Scale Stereo Matching

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Efficient Large-Scale Stereo Matching
ACCV 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.
Andreas Geiger, Martin Roser and Raquel Urtasun
Added 12 Nov 2010
Updated 12 Nov 2010
Type Conference
Year 2010
Where ACCV
Authors Andreas Geiger, Martin Roser and Raquel Urtasun
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