LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909634511503360 |
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| author | Li, Chenxi Chen, Nuo Tan, Fengyun Chen, Yantong Yuan, Bochun Li, Tianrui Li, Chongshou |
| author_facet | Li, Chenxi Chen, Nuo Tan, Fengyun Chen, Yantong Yuan, Bochun Li, Tianrui Li, Chongshou |
| contents | We present a novel active learning framework for 3D point cloud semantic segmentation that, for the first time, integrates large language models (LLMs) to construct hierarchical label structures and guide uncertainty-based sample selection. Unlike prior methods that treat labels as flat and independent, our approach leverages LLM prompting to automatically generate multi-level semantic taxonomies and introduces a recursive uncertainty projection mechanism that propagates uncertainty across hierarchy levels. This enables spatially diverse, label-aware point selection that respects the inherent semantic structure of 3D scenes. Experiments on S3DIS and ScanNet v2 show that our method achieves up to 4% mIoU improvement under extremely low annotation budgets (e.g., 0.02%), substantially outperforming existing baselines. Our results highlight the untapped potential of LLMs as knowledge priors in 3D vision and establish hierarchical uncertainty modeling as a powerful paradigm for efficient point cloud annotation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18924 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning Li, Chenxi Chen, Nuo Tan, Fengyun Chen, Yantong Yuan, Bochun Li, Tianrui Li, Chongshou Computer Vision and Pattern Recognition We present a novel active learning framework for 3D point cloud semantic segmentation that, for the first time, integrates large language models (LLMs) to construct hierarchical label structures and guide uncertainty-based sample selection. Unlike prior methods that treat labels as flat and independent, our approach leverages LLM prompting to automatically generate multi-level semantic taxonomies and introduces a recursive uncertainty projection mechanism that propagates uncertainty across hierarchy levels. This enables spatially diverse, label-aware point selection that respects the inherent semantic structure of 3D scenes. Experiments on S3DIS and ScanNet v2 show that our method achieves up to 4% mIoU improvement under extremely low annotation budgets (e.g., 0.02%), substantially outperforming existing baselines. Our results highlight the untapped potential of LLMs as knowledge priors in 3D vision and establish hierarchical uncertainty modeling as a powerful paradigm for efficient point cloud annotation. |
| title | LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.18924 |