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.

本文提出一种单一和原则性网络，用于联合学习立体匹配和流估计的时空对应关系，通过设计的几何连接作为临近时间内的立体对的无监督信号。结果表明，我们的方法对于 KITTI 基准数据集上的无监督深度和流估计都优于几种最先进的基线方法。