Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark
This is the first benchmark for panoptic segmentation under adverse-to-extreme weather, providing a standardized evaluation for the community.
The paper reports the URVIS 2026 challenge on adverse-to-extreme panoptic segmentation, which attracted 17 participants and 47 submissions. The challenge introduced the MUSES dataset and a new metric (wPQ) to evaluate robustness across weather conditions, with 4 teams reaching the final phase.
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