MfS: Learning Stereo from Single Images

August 2020

tl;dr: Mono for stereo. Learn stereo matching with monocular images.

Overall impression

The basic idea is to generate stereo training pair with mono depth to train stereo matching algorithms. This idea is very similar to that of Homographic Adaptation in SuperPoint, in that both generates training data and GT with known geometric transformation.

This still need a stereo pair as input during inference time. The main idea is to use monodepth to predict a depth map, sharpen it, and generate a stereo pair, with known stereo matching GT.

Key ideas

Technical details