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.
@article{arxiv.2505.18924,
title = {LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning},
author = {Chenxi Li and Nuo Chen and Fengyun Tan and Yantong Chen and Bochun Yuan and Tianrui Li and Chongshou Li},
journal= {arXiv preprint arXiv:2505.18924},
year = {2025}
}