Estimating depth from single RGB images and videos is of widespread interest due to its
applications in many areas, including autonomous driving, 3D reconstruction, digital …

Monocular depth estimation (MDE) is a fundamental task in computer vision and has drawn
increasing attention. Recently, some methods reformulate it as a classification-regression …

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

A lot of research has been conducted in recent years on stereo depth estimation techniques,
taking the traditional approach to a new level such that it is in an appreciably good form for …

Self-supervised space-time correspondence learning utilizing unlabeled videos holds great
potential in computer vision. Most existing methods rely on contrastive learning with mining …

Due to the domain differences and unbalanced disparity distribution across multiple
datasets, current stereo matching approaches are commonly limited to a specific dataset …

Extracting 3D information from a single optical image is very attractive. Recently emerging
self-supervised methods can learn depth representations without using ground truth depth …

The emerging field of combining compressed sensing (CS) and three-dimensional synthetic
aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate …

It is attractive to extract plausible 3-D information from a single 2-D image, and self-
supervised learning has shown impressive potential in this field. However, when only …

Recently, transformers have been widely adopted for various computer vision tasks and
show promising results due to their ability to encode long-range spatial dependencies in an …