Segmentation is a low-level vision cue often deployed by stereo algorithms to assume that disparity within superpixels varies smoothly. In this paper, we show that constraining, on a superpixel basis, the cues provided by a recently proposed technique, which explicitly models local consistency among neighboring points, yields accurate and dense disparity fields. Our proposal, starting from the initial disparity hypotheses of a fast dense stereo algorithm based on scanline optimization, demonstrates its effectiveness by enabling us to obtain results comparable to top-ranked algorithms based on iterative disparity optimization methods.

Accurate dense stereo by constraining local consistency on superpixels

MATTOCCIA, STEFANO
2010

Abstract

Segmentation is a low-level vision cue often deployed by stereo algorithms to assume that disparity within superpixels varies smoothly. In this paper, we show that constraining, on a superpixel basis, the cues provided by a recently proposed technique, which explicitly models local consistency among neighboring points, yields accurate and dense disparity fields. Our proposal, starting from the initial disparity hypotheses of a fast dense stereo algorithm based on scanline optimization, demonstrates its effectiveness by enabling us to obtain results comparable to top-ranked algorithms based on iterative disparity optimization methods.
20th International Conference on Pattern Recognition (ICPR2010)
1832
1835
S. Mattoccia
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/89232
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