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arXiv · 2507.14596

DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF

Doriand Petit·Steve Bourgeois·Vincent Gay-Bellile·Florian Chabot·Loïc Barthe
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Abstract

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.

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Doriand Petit, Steve Bourgeois, Vincent Gay-Bellile, Florian Chabot, Loïc Barthe. 2025-07-19. DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF. https://arxiv.org/abs/2507.14596

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