Revisiting Depth Completion from a Stereo Matching Perspective for
  Cross-domain Generalization
Luca Bartolomei Matteo Poggi Andrea Conti Fabio Tosi Stefano Mattoccia
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. Hence, we argue that our framework can help
depth completion to reach new deployment scenarios.