  Stereo matching and flow estimation are two essential tasks for scene
understanding, spatially in 3D and temporally in motion. Existing approaches
have been focused on the unsupervised setting due to the limited resource to
obtain the large-scale ground truth data. To construct a self-learnable
objective, co-related tasks are often linked together to form a joint
framework. However, the prior work usually utilizes independent networks for
each task, thus not allowing to learn shared feature representations across
models. In this paper, we propose a single and principled network to jointly
learn spatiotemporal correspondence for stereo matching and flow estimation,
with a newly designed geometric connection as the unsupervised signal for
temporally adjacent stereo pairs. We show that our method performs favorably
against several state-of-the-art baselines for both unsupervised depth and flow
estimation on the KITTI benchmark dataset.
