Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

November 2019

tl;dr: Use sparse depth measurement and RGB to generate dense depth map.

Overall impression

The architecture used in this paper is simple. The main innovation is the brand new direction of this paper creates.

RGB-based depth prediction method are unreliable. The addition of ~100 sparse samples can reduce root mean square error by over 50% (on both NYU-v2 and KITTI).

Key ideas

Technical details