CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise
<|reference_start|>CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise: 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.<|reference_end|>
@article{yu2024cus3d,
    title={CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise},
    author={Fuyang Yu, Runze Tian, Zhen Wang, Xiaochuan Wang, Xiaohui Liang},
    journal={arXiv preprint arXiv:2409.13982},
    year={2024},
    doi={10.1109/ICME57554.2024},
    archivePrefix={arXiv},
    eprint={2409.13982},
    primaryClass={cs.CV cs.MM}
}