首个极端天气全景分割挑战赛，多模态数据评估模型鲁棒性
Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark
- 基于多传感器MUSES数据集，测试极端天气下全景分割性能
- 4支队伍进入决赛，权重全景质量(wPQ)为统一评价指标
- 揭示当前多模态融合在恶劣环境中的瓶颈与突破点
本文报告了URVIS 2026关于极端恶劣天气下全景分割的挑战赛。作为同类首项挑战赛，吸引了17个注册团队和47份提交，其中4支队伍进入最终阶段。挑战基于MUSES数据集，该数据集包含RGB相机、激光雷达、雷达和事件相机的多传感器数据，用于在极端天气条件下的全景分割评测。采用加权全景质量（wPQ）作为官方排名指标，确保跨天气条件的公平评估。本文总结了挑战设置与基准结果，分析了各提交方法的性能，并讨论了当前多模态全景分割在鲁棒性方面的进展与现存挑战。
原文摘要 · Abstract (English)
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
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