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 …

Depth estimation is an important task, applied in various methods and applications of
computer vision. While the traditional methods of estimating depth are based on depth cues …

In this paper, we present a fast monocular depth estimation method for enabling 3D
perception capabilities of low-cost underwater robots. We formulate a novel end-to-end …

Depth estimation is an important task in autonomous driving, and usually needs special
types of sensors or multiple cameras. In this paper, we propose a novel approach to …

Test-time domain adaptation, ie adapting source-pretrained models to the test data on-the-
fly in a source-free, unsupervised manner, is a highly practical yet very challenging task …

Monocular depth estimation is challenging due to its inherent ambiguity and ill-posed
nature, yet it is quite important to many applications. While recent works achieve limited …

Although both self-supervised single-frame and multi-frame depth estimation methods only
require unlabeled monocular videos for training, the information they leverage varies …

Semi-supervised learning via teacher-student network can train a model effectively on a few
labeled samples. It enables a student model to distill knowledge from the teacher's …

Multi-task learning promises better model generalization on a target task by jointly
optimizing it with an auxiliary task. However, the current practice requires additional labeling …

Transferring the absolute depth prediction capabilities of an estimator to a new domain is a
task with significant real-world applications. This task is specifically challenging when …