# LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning Source: [LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:52:00.639Z Authors: Chenxi Li, Nuo Chen, Fengyun Tan, Yantong Chen, Bochun Yuan, Tianrui Li, Chongshou Li Published: 2025-05-25T01:10:58.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()