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

Yiting Wang; Nolwenn Peyratout; Tim Brodermann; Jiahui Wang; Yusi Cao; Michele Cazzola; Elie Tarassov; Takuya Kobayashi

cs.CVApr 18, 2026

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

Authors: Yiting Wang, Nolwenn Peyratout, Tim Brodermann, Jiahui Wang, Yusi Cao, Michele Cazzola, Elie Tarassov, Takuya Kobayashi, +8 more

Organizations: Organizers of the URVIS 2026 Challenge

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

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May 21, 2026 · cs.CVJ/K move · Enter open · S save

A Robust Semantic Segmentation Pipeline for the CVPR 2026 8th UG2+ Challenge Track 2

Jinming Chai, Libo Yan, Licheng Jiao, Fang Liu

School of Artificial Intelligence, Xidian University, Xi’an 710071, China

This report presents our solution for the WeatherProof Dataset Challenge, namely CVPR 2026 8th UG2+ Challenge Track 2: Semantic Segmentation in Adverse Weather. For the semantic segmentation task under adverse weather conditions, we propose a semi-supervised segmentation pipeline. Our method is trained exclusively on the WeatherProof dataset, without using any additional external data. Specifically, we adopt UniMatch V2 as the baseline model and treat all degraded-weather images as unlabeled data for semi-supervised training, thereby fully exploiting the data distribution provided by the challenge. During inference, we further apply test-time augmentation to improve the robustness and segmentation accuracy of the final predictions. The code is publicly available at: https://github.com/ylb888/weatherproof-challenge-unimatchv2.

Dense Semantic Segmentation Cvpr Semi-Supervised Learning

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Abstract

Explore similar work

A Robust Semantic Segmentation Pipeline for the CVPR 2026 8th UG2+ Challenge Track 2

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