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

Source: [DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF](<https://www.sota2.com/research/paper/68fa7a141e209131cea81001>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:11:23.415Z

Authors: Doriand Petit, Steve Bourgeois, Vincent Gay-Bellile, Florian Chabot, Lo\\"ic Barthe

Published: 2025-07-19T12:46:20.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2507.14596>)

[PDF](<https://arxiv.org/pdf/2507.14596.pdf>)