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Recently, there has been a paradigm shift in stereo matching withlearning-based methods achieving the best results on all popular benchmarks.The success of these methods is due to the availability of training data withground truth; training learning-based systems on these datasets has allowedthem to surpass the accuracy of conventional approaches based on heuristics andassumptions. Many of these assumptions, however, had been validated extensivelyand hold for the majority of possible inputs. In this paper, we generate amatching volume leveraging both data with ground truth and conventional wisdom.We accomplish this by coalescing diverse evidence from a bidirectional matchingprocess via random forest classifiers. We show that the resulting matchingvolume estimation method achieves similar accuracy to purely data-drivenalternatives on benchmarks and that it generalizes to unseen data much better.In fact, the results we submitted to the KITTI and ETH3D benchmarks weregenerated using a classifier trained on the Middlebury 2014 dataset.

CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation