Learning Depth Estimation for Transparent and Mirror Surfaces
Alex Costanzino Pierluigi Zama Ramirez Matteo Poggi Fabio Tosi Stefano Mattoccia Luigi Di Stefano
Inferring the depth of transparent or mirror (ToM) surfaces represents a hard
challenge for either sensors, algorithms, or deep networks. We propose a simple
pipeline for learning to estimate depth properly for such surfaces with neural
networks, without requiring any ground-truth annotation. We unveil how to
obtain reliable pseudo labels by in-painting ToM objects in images and
processing them with a monocular depth estimation model. These labels can be
used to fine-tune existing monocular or stereo networks, to let them learn how
to deal with ToM surfaces. Experimental results on the Booster dataset show the
dramatic improvements enabled by our remarkably simple proposal.