CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching

Understand

Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound progress in stereo matching.

• However, most of these successes are limited to a specific dataset and cannot generalize well to other datasets.
• The main difficulties lie in the large domain differences and unbalanced disparity distribution across a variety of datasets, which greatly limit the real-world applicability of current deep stereo matching models.
• In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network.