Multi-view stereo reconstruction (MVS) in the wild requires to first estimate the camera
intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain yet …

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 …

Given sparse depths and the corresponding RGB images, depth completion aims at spatially
propagating the sparse measurements throughout the whole image to get a dense depth …

Depth estimation from monocular images is pivotal for real-world visual perception systems.
While current learning-based depth estimation models train and test on meticulously curated …

Abstract Three-dimensional (3D) reconstruction from a single image is an ill-posed problem
with inherent ambiguities ie scale. Predicting a 3D scene from text description (s) is similarly …

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

Current, self-supervised depth estimation architectures rely on clear and sunny weather
scenes to train deep neural networks. However, in many locations, this assumption is too …

In the realm of autonomous driving, robust perception under out-of-distribution conditions is
paramount for the safe deployment of vehicles. Challenges such as adverse weather …

Recently, self-supervised monocular depth estimation has gained popularity with numerous
applications in autonomous driving and robotics. However, existing solutions primarily seek …

This paper tackles the challenges of self-supervised monocular depth estimation in indoor
scenes caused by large rotation between frames and low texture. We ease the learning …