Adverse weather and illumination conditions (e.g. fog, rain, snow, low light, nighttime, glare and shadows) create visibility problems for the sensors that power automated systems. Many outdoor applications such as autonomous cars and surveillance systems are required to operate smoothly in the frequent scenarios of bad weather. While rapid progress is being made in this direction, the performance of current vision algorithms is still mainly benchmarked under clear weather conditions (good weather, favorable lighting). Even the top-performing algorithms undergo a severe performance degradation under adverse conditions. The aim of this workshop is to promote research into the design of robust vision algorithms for adverse weather and lighting conditions.
Invited Speakers for V4AS@CVPR’23

Daniel Cremers
TU Munich

Judy Hoffman
Georgia Tech

Felix Heide
Princeton University

Adam Kortylewski
MPI for Informatics

Werner Ritter
Mercedes-Benz AG

Patrick Pérez
valeo.ai

Robby T. Tan
NUS

Eren Erdal Aksoy
Halmstad University

Tim Barfoot
University of Toronto
Organizers

Dengxin Dai
Huawei Zurich

Christos Sakaridis
ETH Zurich

Lukas Hoyer
ETH Zurich

Haoran Wang
MPI for Informatics

Wim Abbeloos
Toyota Motor Europe

Daniel Olmeda Reino
Toyota Motor Europe

Jiri Matas
CTU in Prague

Bernt Schiele
MPI for Informatics

Luc Van Gool
ETH Zurich & KU Leuven