综述激光雷达点云中人体姿态与建模方法，梳理技术路线与评估标准。
3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review
- 构建分类体系，系统归纳现有方法的技术路径。
- 对比三大主流数据集，统一评估指标定义并建立基准表。
- 适合从事三维人体感知、自动驾驶场景理解的研究者参考。
本文全面回顾了从野外环境激光雷达点云中进行3D人体姿态估计与人体网格重建的研究进展。我们从多个关键维度对比现有方法，并提出一种结构化分类体系以归类这些方法。基于该分类体系，分析各方法的优势、局限及设计选择。此外，(i) 对三个最常用数据集进行了定量比较，详细描述其特性；(ii) 统一定义了所有评估指标；(iii) 在这些数据集上建立人体姿态与网格重建的基准表格，以促进公平比较并推动领域发展。文章还指出了当前面临的关键挑战与未来研究方向，助力基于激光雷达的3D人体理解进步。相关论文按分类体系整理于配套网页，持续更新：https://github.com/valeoai/3D-Human-Pose-Shape-Estimation-from-LiDAR
原文摘要 · Abstract (English)
In this paper, we present a comprehensive review of 3D human pose estimation and human mesh recovery from in-the-wild LiDAR point clouds. We compare existing approaches across several key dimensions, and propose a structured taxonomy to classify these methods. Following this taxonomy, we analyze each method's strengths, limitations, and design choices. In addition, (i) we perform a quantitative comparison of the three most widely used datasets, detailing their characteristics; (ii) we compile unified definitions of all evaluation metrics; and (iii) we establish benchmark tables for both tasks on these datasets to enable fair comparisons and promote progress in the field. We also outline open challenges and research directions critical for advancing LiDAR-based 3D human understanding. Moreover, we maintain an accompanying webpage that organizes papers according to our taxonomy and continuously update it with new studies: https://github.com/valeoai/3D-Human-Pose-Shape-Estimation-from-LiDAR
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。