发表机构
The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TAP3D是首个从人体热信号重建三维人体点云的系统，采用物理信息设计，在多任务中表现优异，开创了隐私优先的被动人体感知新范式。
AI 中文摘要
人体点云是AI驱动人体感知的通用表示形式，但现有使用激光雷达、雷达和深度相机的方法存在成本高、重建稀疏、隐私问题等固有缺陷。本文利用低成本热阵列，提出首个从人体热信号重建三维人体点云的系统TAP3D，在成本、密度、人体敏感性和隐私性方面具有显著优势。为克服深度估计、热干扰和多人分离的主要挑战，我们提出一种新颖的物理信息设计，将正向热物理模型与两个不同模块集成：用于自监督恢复深度和其他热属性的多基元估计模块，以及用于抑制干扰和解离多人的几何视角融合模块。我们使用单个商用热阵列传感器实现TAP3D，并构建包含160K样本、8种环境、11名用户的大规模数据集进行评估。TAP3D在密集点云生成方面实现了显著精度，支持跌倒检测（91.46%）、室内跟踪（21.86 cm MAE）和人体网格恢复（4.87 cm误差）等下游任务。通过首次将人体热转化为点云，TAP3D开创了隐私优先、完全被动人体感知的新范式，适用于众多应用，TAP3D已开源。
英文摘要
Human body point clouds are a versatile representation for AI-enabled human sensing. However, existing methods using LiDAR, radar, and depth cameras suffer from inherent drawbacks in high cost, sparse reconstruction, and privacy concerns, etc. In this paper, we exploit low-cost thermal arrays and present TAP3D, the first system to reconstruct 3D human point clouds from body heat signatures, offering significant advantages in cost, density, human sensitivity, and privacy. To overcome major challenges in depth estimation, thermal interference, and multi-person separation, we propose a novel physics-informed design, which integrates a forward thermal physics model with two distinct modules: multi-primitive estimation for self-supervised joint recovery of depth and other thermal properties, and geometric perspective fusion for suppressing interference and disentangling multiple people. We implement TAP3D using a single commodity thermal array sensor and build a large-scale dataset (160K samples, 8 environments, 11 users) for evaluation. TAP3D achieves remarkable accuracy for dense point cloud generation, enabling downstream tasks like fall detection (91.46%), indoor tracking (21.86 cm MAE), and human mesh recovery (4.87 cm error). By transforming body heat into point clouds for the first time, TAP3D pioneers a new paradigm for privacy-first, fully passive human sensing for many applications. TAP3D is open-sourced at https://github.com/aiot-lab/TAP3D.
