  End-to-end deep-learning networks recently demonstrated extremely good
perfor- mance for stereo matching. However, existing networks are difficult to
use for practical applications since (1) they are memory-hungry and unable to
process even modest-size images, (2) they have to be trained for a given
disparity range. The Practical Deep Stereo (PDS) network that we propose
addresses both issues: First, its architecture relies on novel bottleneck
modules that drastically reduce the memory footprint in inference, and
additional design choices allow to handle greater image size during training.
This results in a model that leverages large image context to resolve matching
ambiguities. Second, a novel sub-pixel cross- entropy loss combined with a MAP
estimator make this network less sensitive to ambiguous matches, and applicable
to any disparity range without re-training. We compare PDS to state-of-the-art
methods published over the recent months, and demonstrate its superior
performance on FlyingThings3D and KITTI sets.
