Self-Supervised Depth Estimation with Isometric-Self-Sample-Based Learning. (arXiv:2205.10006v1 [cs.CV])

Managing the dynamic regions in the photometric loss formulation has been a
main issue for handling the self-supervised depth estimation problem. Most
previous methods have alleviated this issue by removing the dynamic regions in
the photometric loss formulation based on the masks estimated from another
module, making it difficult to fully utilize the training images. In this
paper, to handle this problem, we propose an isometric self-sample-based
learning (ISSL) method to fully utilize the training images in a simple yet …

arxiv cv learning