Reconstructing accurate 3D scenes from images is a long-standing vision task. Due to the ill-
posedness of the single-image reconstruction problem, most well-established methods are …

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and
Transparent surfaces held in conjunction with the New Trends in Image Restoration and …

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 …

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 …

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse
weather conditions, sensor failure, and noise contamination, is desirable for safety-critical …

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 introduces PatchRefiner, an advanced framework for metric single image depth
estimation aimed at high-resolution real-domain inputs. While depth estimation is crucial for …

This paper discusses the results of the third edition of the Monocular Depth Estimation
Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging …

Visibility affects traffic flow and control on city roads, highways, and runways. Visibility
distance or level is an important measure for predicting the risk on the road. Particularly, it is …

We present a novel approach designed to address the complexities posed by challenging,
out-of-distribution data in the single-image depth estimation task. Starting with images that …