Estimating the accurate depth from a single image is challenging since it is inherently
ambiguous and ill-posed. While recent works design increasingly complicated and powerful …

Self-supervised monocular depth estimation (SS-MDE) has the potential to scale to vast
quantities of data. Unfortunately, existing approaches limit themselves to the automotive …

This paper discusses the results for the second edition of the Monocular Depth Estimation
Challenge (MDEC). This edition was open to methods using any form of supervision …

This paper summarizes the results of the first Monocular Depth Estimation Challenge
(MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised …

Robots are active agents that operate in dynamic scenarios with noisy sensors. Predictions
based on these noisy sensor measurements often lead to errors and can be unreliable. To …

Stereo matching is close to hitting a half-century of history, yet witnessed a rapid evolution in
the last decade thanks to deep learning. While previous surveys in the late 2010s covered …

Over recent years, there has been an increase in research interest regarding depth
estimation using multiple-spectrum images from Visible-Light (VIS) and Thermal-Infrared …

Self-supervised learning is the key to unlocking generic computer vision systems. By
eliminating the reliance on ground-truth annotations, it allows scaling to much larger data …

We present a portable multiscopic camera system with a dedicated model for novel view and
time synthesis in dynamic scenes. Our goal is to render high-quality images for a dynamic …

Monocular depth estimation is a challenging problem on which deep neural networks have
demonstrated great potential. However, depth maps predicted by existing deep models …