Domain-invariant Stereo Matching Networks

Understand

State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast, and texture.

• In this paper, we aim at designing a domain-invariant stereo matching network (DSMNet) that generalizes well to unseen scenes.
• To achieve this goal, we propose i) a novel "domain normalization" approach that regularizes the distribution of learned representations to allow them to be invariant to domain differences, and ii) a trainable non-local graph-based filter for extracting robust structural and geometric representations that can further enhance domain-invariant generalizations.
• When trained on synthetic data and generalized to real test sets, our model performs significantly better than all state-of-the-art models.