Ensemble Classifier for Combining Stereo Matching Algorithms

Stereo matching, as many problems in computer vision, has been addressed by a multitude of algorithms, each with its own strengths and weaknesses. Instead of following the conventional approach and trying to tune or enhance one of the algorithms so that it dominates the competition, we resign to the idea that a truly optimal algorithm may not be discovered soon and take a different approach. We present a novel methodology for combining a large number of heterogeneous algorithms that is able to clearly surpass the accuracy of the most accurate algorithms in the set. At the core of our approach is the design of an ensemble classifier trained to decide whether a particular stereo matcher is correct on a certain pixel. In addition to features describing the pixel, our feature vector encodes the agreement and disagreement between the matcher under consideration and all other matchers. This formulation leads to high accuracy in disparity estimation on the KITTI stereo benchmark.