mlsanityData to 14 Oct 2026

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

arXiv 2507.14596 ↗First posted 19 Jul 2025cs.CVcited by 2

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Abstract (arXiv)

3D semantic segmentation provides high-level scene understanding for applications in robotics, autonomous systems, textitetc. 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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 37 of its 42 references that are in the map. First posted on arXiv 19 Jul 2025.

Builds on

37 of 42 references.

References · 37

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25 more
  1. 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Visioncited by 537
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025LLaVA3: Representing 3D Scenes like a Cubist Painter to Boost 3D Scene Understanding of VLMscited by 1
  2. 2026VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentationcited by 0