  We explore the problem of real-time stereo matching on high-res imagery. Many
state-of-the-art (SOTA) methods struggle to process high-res imagery because of
memory constraints or speed limitations. To address this issue, we propose an
end-to-end framework that searches for correspondences incrementally over a
coarse-to-fine hierarchy. Because high-res stereo datasets are relatively rare,
we introduce a dataset with high-res stereo pairs for both training and
evaluation. Our approach achieved SOTA performance on Middlebury-v3 and
KITTI-15 while running significantly faster than its competitors. The
hierarchical design also naturally allows for anytime on-demand reports of
disparity by capping intermediate coarse results, allowing us to accurately
predict disparity for near-range structures with low latency (30ms). We
demonstrate that the performance-vs-speed trade-off afforded by on-demand
hierarchies may address sensing needs for time-critical applications such as
autonomous driving.
