Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization

Understand

This paper proposes a new framework for depth completion robust against domain-shifting issues.

• It exploits the generalization capability of modern stereo networks to face depth completion, by processing fictitious stereo pairs obtained through a virtual pattern projection paradigm.
• Any stereo network or traditional stereo matcher can be seamlessly plugged into our framework, allowing for the deployment of a virtual stereo setup that is future-proof against advancement in the stereo field.
• Exhaustive experiments on cross-domain generalization support our claims.