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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.16984 |
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| _version_ | 1866917418017751040 |
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| author | Wang, Yiting Peyratout, Nolwenn Brodermann, Tim Wang, Jiahui Cao, Yusi Cazzola, Michele Tarassov, Elie Kobayashi, Takuya Kasmi, Abderrahim Allibert, Guillaume Demonceaux, Cédric Donzella, Valentina Debattista, Kurt Timofte, Radu Wu, Zongwei Sakaridis, Christos |
| author_facet | Wang, Yiting Peyratout, Nolwenn Brodermann, Tim Wang, Jiahui Cao, Yusi Cazzola, Michele Tarassov, Elie Kobayashi, Takuya Kasmi, Abderrahim Allibert, Guillaume Demonceaux, Cédric Donzella, Valentina Debattista, Kurt Timofte, Radu Wu, Zongwei Sakaridis, Christos |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16984 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark Wang, Yiting Peyratout, Nolwenn Brodermann, Tim Wang, Jiahui Cao, Yusi Cazzola, Michele Tarassov, Elie Kobayashi, Takuya Kasmi, Abderrahim Allibert, Guillaume Demonceaux, Cédric Donzella, Valentina Debattista, Kurt Timofte, Radu Wu, Zongwei Sakaridis, Christos Computer Vision and Pattern Recognition 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 |
| title | Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.16984 |