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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.14596 |
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| _version_ | 1866911066532872192 |
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| author | Petit, Doriand Bourgeois, Steve Gay-Bellile, Vincent Chabot, Florian Barthe, Loïc |
| author_facet | Petit, Doriand Bourgeois, Steve Gay-Bellile, Vincent Chabot, Florian Barthe, Loïc |
| contents | 3D semantic segmentation provides high-level scene understanding for applications in robotics, autonomous systems, \textit{etc}. Traditional methods adapt exclusively to either task-specific goals (open-vocabulary segmentation) or scene content (unsupervised semantic segmentation). We propose DiSCO-3D, the first method addressing the broader problem of 3D Open-Vocabulary Sub-concepts Discovery, which aims to provide a 3D semantic segmentation that adapts to both the scene and user queries. We build DiSCO-3D on Neural Fields representations, combining unsupervised segmentation with weak open-vocabulary guidance. Our evaluations demonstrate that DiSCO-3D achieves effective performance in Open-Vocabulary Sub-concepts Discovery and exhibits state-of-the-art results in the edge cases of both open-vocabulary and unsupervised segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14596 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF Petit, Doriand Bourgeois, Steve Gay-Bellile, Vincent Chabot, Florian Barthe, Loïc Computer Vision and Pattern Recognition 3D semantic segmentation provides high-level scene understanding for applications in robotics, autonomous systems, \textit{etc}. Traditional methods adapt exclusively to either task-specific goals (open-vocabulary segmentation) or scene content (unsupervised semantic segmentation). We propose DiSCO-3D, the first method addressing the broader problem of 3D Open-Vocabulary Sub-concepts Discovery, which aims to provide a 3D semantic segmentation that adapts to both the scene and user queries. We build DiSCO-3D on Neural Fields representations, combining unsupervised segmentation with weak open-vocabulary guidance. Our evaluations demonstrate that DiSCO-3D achieves effective performance in Open-Vocabulary Sub-concepts Discovery and exhibits state-of-the-art results in the edge cases of both open-vocabulary and unsupervised segmentation. |
| title | DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.14596 |