Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

Abstract Neural Radiance Fields (NeRF) have recently gained a surge of interest within the
computer vision community for its power to synthesize photorealistic novel views of real …

Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this
limitation, self-supervised learning has emerged as a promising alternative for training …

Virtual reality and augmented reality (XR) bring increasing demand for 3D content
generation. However, creating high-quality 3D content requires tedious work from a human …

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 …

Estimating accurate depth from a single image is challenging because it is an ill-posed
problem as infinitely many 3D scenes can be projected to the same 2D scene. However …

Recent work has shown that CNN-based depth and ego-motion estimators can be learned
using unlabelled monocular videos. However, the performance is limited by unidentified …

Although cameras are ubiquitous, robotic platforms typically rely on active sensors like
LiDAR for direct 3D perception. In this work, we propose a novel self-supervised monocular …

Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene
information from single camera images, which is trainable on arbitrary image sequences …