Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

Self-supervised monocular depth estimation that does not require ground truth for training
has attracted attention in recent years. It is of high interest to design lightweight but effective …

Self-supervised monocular depth estimation is an attractive solution that does not require
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …

Neural networks have shown great abilities in estimating depth from a single image.
However, the inferred depth maps are well below one-megapixel resolution and often lack …

We propose a monocular depth estimation method SC-Depth, which requires only
unlabelled videos for training and enables the scale-consistent prediction at inference time …

While state-of-the-art monocular depth estimation approaches achieve impressive results in
ideal settings, they are highly unreliable under challenging illumination and weather …

Self-supervised learning for depth estimation uses geometry in image sequences for
supervision and shows promising results. Like many computer vision tasks, depth network …

Abstract Neural Radiance Fields (NeRFs) have shown remarkable success in synthesizing
photorealistic views from multi-view images of static scenes but face challenges in dynamic …

Self-supervised monocular depth estimation has been widely studied, owing to its practical
importance and recent promising improvements. However, most works suffer from limited …