Potholes pose a significant problem for road safety and infrastructure. They can cause
damage to vehicles and present a risk to pedestrians and cyclists. The ability to detect …

We introduce a set of image transformations that can be used as corruptions to evaluate the
robustness of models as well as data augmentation mechanisms for training neural …

Abstract 3D object detection is a central task for applications such as autonomous driving, in
which the system needs to localize and classify surrounding traffic agents, even in the …

This work addresses the challenging task of LiDAR-based 3D object detection in foggy
weather. Collecting and annotating data in such a scenario is very time, labor and cost …

How to ensure robust and accurate 3D object detection under various environment is
essential for autonomous driving (AD) environment perception. While, until now, most of the …

Automated vehicles rely heavily on image data from visible spectrum cameras to perform a
wide range of tasks from object detection, classification, and avoidance to path planning …

Although deep neural networks enable impressive visual perception performance for
autonomous driving, their robustness to varying weather conditions still requires attention …

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 …

Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse
visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) …

Rain can significantly degrade image quality and visibility, making deraining a critical area
of research in computer vision. Despite recent progress in learning-based deraining …