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.
S. Mattoccia, "Accurate dense stereo by constraining local consistency on superpixels"
, 20th International Conference on Pattern Recognition (ICPR2010
), August 23-26, 2010, Istanbul, Turkey [Pdf]
results according to the Middlebury automatic evaluation [2,3]
deploying the initial disparity hypotheses provided by algorithm ,
available in  and referred to as DS
. The output of the proposed algorithm is referred to as D(3)
. Further details can be found in the paper and in the slides linked at the bottom of this page.
Evaluation on the Middlebury dataset
Experimental results on the Venus stereo pair
experimental results on the Venus stereo pair [2,3] using the initial
disparity hypothesses provided by  and available on the same
website. For segmentation I deployed the Mean-Shift algorithm 
(source code available in ).
Target Initial disparity hypotheses DS
After LC  Oversegmentation [5,6]
Relaxing segmentation [5,6]