DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos
Hualie Jiang,Laiyan Ding,Zhenglong Sun,Rui Huang,Hualie Jiang,Laiyan Ding,Zhenglong Sun,Rui Huang
Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised one. It achieves this by using the photometric errors between the target view and the synthesized views from its adjacent source views as the loss. Despite significant progress, the learning still suffers from occlusion ...
