Learning to predict stereo reliability enforcing
local consistency of confidence maps
Abstract
Confidence measures estimate unreliable disparity assignments performed by a stereo matching algorithm and,
as recently proved, can be used for several purposes. This
paper aims at increasing, by means of a deep network,
the effectiveness of state-of-the-art confidence measures exploiting the local consistency assumption. We exhaustively
evaluated our proposal on 23 confidence measures, including 5 top-performing ones based on random-forests and
CNNs, training our networks with two popular stereo algorithms and a small subset (25 out of 194 frames) of the
KITTI 2012 dataset. Experimental results show that our approach dramatically increases the effectiveness of all the 23
confidence measures on the remaining frames. Moreover,
without re-training, we report a further cross-evaluation
on KITTI 2015 and Middlebury 2014 confirming that our
proposal provides remarkable improvements for each confi-
dence measure even when dealing with significantly different input data. To the best of our knowledge, this is the first
method to move beyond conventional pixel-wise confidence
estimation