LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning

• URL: http://arxiv.org/abs/2505.18924v2
• Date: Tue, 03 Jun 2025 14:11:24 GMT
• Title: LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning
• Abstract summary: We present a novel active learning framework for 3D point cloud semantic segmentation.<n>It integrates large language models (LLMs) to construct hierarchical label structures and guide uncertainty-based sample selection.<n>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.
• Score: 21.291028259534308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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