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 im- proving 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 fa- vor 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 bleed- ing. Its good performance is also evaluated quanti- tavely by testing the algorithm against the popular Mid- dlebury benchmark where our local greedy implemen- tation is able to obtain results comparable to those of n¨ aive global approaches.