Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network. (arXiv:2308.05605v1 [cs.CV])

Monocular depth estimation is known as an ill-posed task in which objects in
a 2D image usually do not contain sufficient information to predict their
depth. Thus, it acts differently from other tasks (e.g., classification and
segmentation) in many ways. In this paper, we find that self-supervised
monocular depth estimation shows a direction sensitivity and environmental
dependency in the feature representation. But the current backbones borrowed
from other tasks pay less attention to handling different types of
environmental information, limiting …

2d image arxiv classification convolution image information network objects paper segmentation