Confidence-based cost modulation for stereo matching

We present a novel operator to be applied at raw matching costs in the context of low level vision tasks such as stereo matching or optical flow. It aims at improving matching reliability by efficiently modulating pixel-wise pairing costs, injecting a confidence backed bias before the aggregation step. It works analyzing a noisy estimate of the correspondances in order to favor or prune potential matches. We test the operator by developing a local, realtime stereo matching algorithm and showing that our solution can drastically clean the resulting depth map while also reducing border bleeding. Its good performance is also evaluated quantitavely by testing the algorithm against the popular Middlebury benchmark where our local greedy implementation is able to obtain results comparable to those of naive global approaches.