CUS3D：基于CLIP的无监督3D分割：通过物体级去噪
CUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise
AI总结：
本文提出CUS3D框架，通过物体级去噪投影模块和多模态蒸馏学习模块，解决2D到3D特征投影中的噪声问题，实现更准确的无监督和开放词汇3D语义分割。
摘要：
为了缓解3D数据中获取标注标签的困难，一种常见方法是利用2D CLIP语义知识的无监督和开放词汇语义分割。在本文中，不同于以往忽略从2D到3D特征投影过程中产生的“噪声”的研究，我们提出了一种名为CUS3D的新型蒸馏学习框架。在我们的方法中，设计了一个物体级去噪投影模块来筛选出“噪声”并确保更准确的3D特征。基于获得的特征，设计了一个多模态蒸馏学习模块，以物体为中心的约束将3D特征与CLIP语义特征空间对齐，以实现高级的无监督语义分割。我们在无监督和开放词汇分割中进行了全面实验，结果一致展示了我们的模型在实现高级无监督分割结果方面的优越性及其在开放词汇分割中的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
