DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos

3 Mar 2020Hualie JiangLaiyan DingRui Huang

Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn attention as it has notable advantages than the supervised ones. It uses the photometric errors between the target view and the synthesized views from its adjacent source views as the loss... (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.