Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark

April 18, 2026 · Grace Period · + Add venue

Authors Yiting Wang, Nolwenn Peyratout, Tim Brodermann, Jiahui Wang, Yusi Cao, Michele Cazzola, Elie Tarassov, Takuya Kobayashi, Abderrahim Kasmi, Guillaume Allibert, Cédric Demonceaux, Valentina Donzella, Kurt Debattista, Radu Timofte, Zongwei Wu, Christos Sakaridis arXiv ID 2604.16984 Category cs.CV: Computer Vision Citations 0

Abstract

This paper presents the report of the URVIS 2026 challenge on adverse-to-extreme panoptic segmentation. As the first challenge of its kind, it attracted 17 registered participants and 47 submissions, with 4 teams reaching the final phase. The challenge is based on the MUSES dataset, a multi-sensor benchmark for panoptic segmentation in adverse-to-extreme weather, including RGB frame camera, LiDAR, radar, and event camera data. Weighted Panoptic Quality (wPQ) is designed and adopted as the official ranking metric for fair evaluation across weather conditions. In this report, we summarise the challenge setting and benchmark results, analyse the performance of the submitted methods, and discuss current progress and remaining challenges for robust multimodal panoptic segmentation. Link: https://urvis-workshop.github.io/challenge-Muses.html