Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding
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arXiv
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| Format: | Preprint |
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2025
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| author | Mei, Guofeng Ren, Bin Liu, Juan Riz, Luigi Huang, Xiaoshui Zheng, Xu Gong, Yongshun Yang, Ming-Hsuan Sebe, Nicu Poiesi, Fabio |
| author_facet | Mei, Guofeng Ren, Bin Liu, Juan Riz, Luigi Huang, Xiaoshui Zheng, Xu Gong, Yongshun Yang, Ming-Hsuan Sebe, Nicu Poiesi, Fabio |
| contents | Vision-language models like CLIP can offer a promising foundation for 3D scene understanding when extended with 3D tokenizers. However, standard approaches, such as k-nearest neighbor or radius-based tokenization, struggle with cross-domain generalization due to sensitivity to dataset-specific spatial scales. We present a universal 3D tokenizer designed for scale-invariant representation learning with a frozen CLIP backbone. We show that combining superpoint-based grouping with coordinate scale normalization consistently outperforms conventional methods through extensive experimental analysis. Specifically, we introduce S4Token, a tokenization pipeline that produces semantically-informed tokens regardless of scene scale. Our tokenizer is trained without annotations using masked point modeling and clustering-based objectives, along with cross-modal distillation to align 3D tokens with 2D multi-view image features. For dense prediction tasks, we propose a superpoint-level feature propagation module to recover point-level detail from sparse tokens. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18819 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Mei, Guofeng Ren, Bin Liu, Juan Riz, Luigi Huang, Xiaoshui Zheng, Xu Gong, Yongshun Yang, Ming-Hsuan Sebe, Nicu Poiesi, Fabio Computer Vision and Pattern Recognition Vision-language models like CLIP can offer a promising foundation for 3D scene understanding when extended with 3D tokenizers. However, standard approaches, such as k-nearest neighbor or radius-based tokenization, struggle with cross-domain generalization due to sensitivity to dataset-specific spatial scales. We present a universal 3D tokenizer designed for scale-invariant representation learning with a frozen CLIP backbone. We show that combining superpoint-based grouping with coordinate scale normalization consistently outperforms conventional methods through extensive experimental analysis. Specifically, we introduce S4Token, a tokenization pipeline that produces semantically-informed tokens regardless of scene scale. Our tokenizer is trained without annotations using masked point modeling and clustering-based objectives, along with cross-modal distillation to align 3D tokens with 2D multi-view image features. For dense prediction tasks, we propose a superpoint-level feature propagation module to recover point-level detail from sparse tokens. |
| title | Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.18819 |