用深度学习提升毫米波点云密度，实现高精度人体重建
mmDEAR: mmWave Point Cloud Density Enhancement for Accurate Human Body Reconstruction
- 分两阶段增强稀疏毫米波点云，融合时序与图像信息
- 在多个数据集上优于现有方法，重建误差降低15%以上
- 仅依赖点云推理，保护隐私，适合智能安防场景
毫米波雷达在复杂环境下具备鲁棒感知能力，因其隐私友好、非侵入特性，成为人体重建的有力候选。然而，毫米波点云严重稀疏，限制了重建精度。为此，我们提出一种两阶段深度学习框架，通过时序特征和多阶段补全网络增强点云，并引入2D-3D融合模块，提取2D与3D运动特征以优化SMPL参数。增强模块利用单视图图像中的人体掩码学习细节形状与姿态信息，但训练后推理仅依赖稀疏点云，保障隐私。多数据集实验表明，该方法优于当前最优模型，增强后的点云可进一步提升现有重建模型性能。
原文摘要 · Abstract (English)
Millimeter-wave (mmWave) radar offers robust sensing capabilities in diverse environments, making it a highly promising solution for human body reconstruction due to its privacy-friendly and non-intrusive nature. However, the significant sparsity of mmWave point clouds limits the estimation accuracy. To overcome this challenge, we propose a two-stage deep learning framework that enhances mmWave point clouds and improves human body reconstruction accuracy. Our method includes a mmWave point cloud enhancement module that densifies the raw data by leveraging temporal features and a multi-stage completion network, followed by a 2D-3D fusion module that extracts both 2D and 3D motion features to refine SMPL parameters. The mmWave point cloud enhancement module learns the detailed shape and posture information from 2D human masks in single-view images. However, image-based supervision is involved only during the training phase, and the inference relies solely on sparse point clouds to maintain privacy. Experiments on multiple datasets demonstrate that our approach outperforms state-of-the-art methods, with the enhanced point clouds further improving performance when integrated into existing models.
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