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