  We present TemporalStereo, a coarse-to-fine stereo matching network that is
highly efficient, and able to effectively exploit the past geometry and context
information to boost matching accuracy. Our network leverages sparse cost
volume and proves to be effective when a single stereo pair is given. However,
its peculiar ability to use spatio-temporal information across stereo sequences
allows TemporalStereo to alleviate problems such as occlusions and reflective
regions while enjoying high efficiency also in this latter case. Notably, our
model -- trained once with stereo videos -- can run in both single-pair and
temporal modes seamlessly. Experiments show that our network relying on camera
motion is robust even to dynamic objects when running on videos. We validate
TemporalStereo through extensive experiments on synthetic (SceneFlow,
TartanAir) and real (KITTI 2012, KITTI 2015) datasets. Our model achieves
state-of-the-art performance on any of these datasets. Code is available at
\url{https://github.com/youmi-zym/TemporalStereo.git}.
