# CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise

Source: [CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise](<https://www.sota2.com/research/paper/68fa75341e209131cea6662e>)

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

Data updated: 2025-10-27T06:41:00.437Z

Authors: Fuyang Yu, Runze Tian, Zhen Wang, Xiaochuan Wang, Xiaohui Liang

Published: 2024-09-21T02:17:35.000Z

## Abstract

To ease the difficulty of acquiring annotation labels in 3D data, a common method is using unsupervised and open-vocabulary semantic segmentation, which leverage 2D CLIP semantic knowledge. In this paper, unlike previous research that ignores the \`\`noise'' raised during feature projection from 2D to 3D, we propose a novel distillation learning framework named CUS3D. In our approach, an object-level denosing projection module is designed to screen out the \`\`noise'' and ensure more accurate 3D feature. Based on the obtained features, a multimodal distillation learning module is designed to align the 3D feature with CLIP semantic feature space with object-centered constrains to achieve advanced unsupervised semantic segmentation. We conduct comprehensive experiments in both unsupervised and open-vocabulary segmentation, and the results consistently showcase the superiority of our model in achieving advanced unsupervised segmentation results and its effectiveness in open-vocabulary 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/2409.13982>)

[PDF](<https://arxiv.org/pdf/2409.13982.pdf>)