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160
Voted
ICPR
2010
IEEE
2010
IEEE
Accurate Dense Stereo by Constraining Local Consistency on Superpixels
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.
Real-time TrafficComputer Vision | ICPR 2010 | Initial Disparity Hypotheses | Iterative Disparity Optimization | Stereo Algorithm |
| Added | 02 Sep 2010 |
| Updated | 02 Sep 2010 |
| Type | Conference |
| Year | 2010 |
| Where | ICPR |
| Authors | Stefano Mattoccia |
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