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

arXiv cs.CV / 4/21/2026

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Key Points

The URVIS 2026 challenge introduced an adverse-to-extreme panoptic segmentation benchmark, aiming to tackle robust understanding in very challenging weather conditions.
The event drew 17 registered participants, received 47 submissions, and saw 4 teams reach the final stage, making it the first challenge of its kind.
The benchmark is built on the MUSES dataset, combining multi-sensor inputs (RGB cameras, LiDAR, radar, and event cameras) to cover adverse-to-extreme weather scenarios.
Weighted Panoptic Quality (wPQ) was selected as the official ranking metric to enable fair comparisons across different weather conditions.
The report summarizes the challenge setup and results, analyzes submitted methods, and highlights remaining gaps for robust multimodal panoptic segmentation.

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