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.

提出了实用深层立体(PDS)网络，使用瓶颈模块和新的子像素交叉熵损失和MAP估计器，使其具有更小的内存占用，可处理更大的图像，且不需要重新训练就可适用于任何视差范围，从而在FlyingThings3D和KITTI数据集上取得了优越性能。

实用的深度视差技术(PDS)：面向应用友好的深度视差匹配