{
  "paper_id": "5167aa0a9d2addd7e64c80f8311d3c40",
  "paper_title": "3D human pose point cloud data of light detection and ranging (LiDAR)",
  "paper_authors": [
    "Farah Zakiyah Rahmanti",
    "Moch. Iskandar Riansyah",
    "Oddy Virgantara Putra",
    "Eko Mulyanto Yuniarno",
    "Mauridhi Hery Purnomo"
  ],
  "publication_date": "2025-09-10",
  "publication_venue": "Data in Brief",
  "abstract": "3D Light Detection and Ranging (LiDAR) sensors are closely related to computer vision and deep learning. 3D LiDAR sensors are commonly embedded in smart vehicles to segment humans, cars, trucks, motors, and other objects. However, 3D LiDAR can also be used indoors to predict human poses that are more friendly to a person's privacy because 3D LiDAR does not capture facial images, but it produces data in the form of point clouds. The point cloud produces spatial, geometric, and temporal information which can be used to predict, detect, and classify human poses and activities. The data output from 3D LiDAR, which includes spatial and temporal data, is in PCAP (.pcap) and JSON (.json) formats. The PCAP file contains the sequence frame of the 3D human pose point cloud, and the JSON file contains the metadata. Each human pose class label has one PCAP file and one JSON file. The raw spatio-temporal data must be processed into PCD format as a 3D human pose point cloud dataset for each human pose. The total human pose dataset is 1400 3D point cloud data with PCD format (.pcd) used for the training and testing process in deep learning, consisting of four human pose labels. The label classes are hands-to-the-side, sit-down, squat-down, and stand-up human poses, with each class having 280 3D point cloud data used as training data. While the test data amounted to 280 3D point cloud data. The data collection process uses 3D LiDAR, a tripod, a personal computer/laptop, and a talent, demonstrating basic human poses. The 3D LiDAR used is OS1, a product of Ouster, which has a range of 90–200 m, 128 channels of resolution, and a temperature of -40 ~ 60°C. For talent, there is one person and male gender in this current shooting. However, in its development, it can also take female or children or elderly talent to enrich the human pose dataset. The talent is between 30 and 40 years old. The distance between the 3D LiDAR and the talent position is 120 cm. Data collection took place from 10:00 a.m. to 1:00 pm. indoors. This dataset is used for human pose prediction using one of the deep learning algorithms, Convolutional Neural Network (CNN). However, the developers can also use other deep learning algorithms such as transformers, Graph Neural Network (GNN), etc.",
  "keywords": [
    "3D LiDAR",
    "Human pose prediction",
    "Spatial",
    "Geometric",
    "Temporal",
    "3D point cloud dataset"
  ],
  "paper_institutions": [
    "Institut Teknologi Sepuluh Nopember",
    "Telkom University Surabaya Campus",
    "Universitas Darussalam Gontor"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:47:14.776446Z",
  "template_tag": "simple",
  "summary": "### 摘要\n本数据集论文针对室内三维人体姿态预测中缺乏公开点云数据集的问题，提出了一个基于3D LiDAR传感器采集的、包含空间、几何和时间信息的3D人体姿态点云数据集。该数据集使用Ouster OS1 3D LiDAR（128通道，测距90–200米）在室内对一名30–40岁男性采集，LiDAR与人体距离120厘米，覆盖双手侧举、坐下、下蹲和站立四种基本姿态。原始数据以PCAP和JSON格式存储，经过3D裁剪分割和人工标注，转换为PCD格式，共1400个点云样本，其中每类280个共1120个训练样本，另有280个测试样本，类别均衡。该数据集可用于基于CNN、Transformer或GNN等深度学习算法的人体姿态预测、姿态估计和活动识别，尤其适用于隐私敏感场景（如浴室、卧室）和低光照或逆光条件下的异常检测。\n\n### 关键要点\n- 数据集包含1400个3D点云样本，涵盖双手侧举、坐下、下蹲和站立四种人体姿态类别，每类280个样本，训练与测试数据比例为80%和20%。\n- 使用Ouster OS1 3D LiDAR传感器采集，具有128通道分辨率、90–200米测距范围和-40至60°C工作温度。\n- 数据采集在室内进行，LiDAR与人体距离固定为120厘米，采集时间从上午10点到下午1点。\n- 原始数据格式为PCAP和JSON，经过去噪、分割和人工标注后转换为PCD格式，便于深度学习模型直接使用。\n- 数据提供空间信息（3D坐标x, y, z）、几何信息（身体关节、姿态、体型、朝向）和时间信息（序列帧、时长、姿态转换）。\n- 该数据集可作为健康成年人基本姿态的基准，用于物理治疗、运动分析、老年人跌倒检测等健康监测应用。\n- 数据集具有隐私保护优势，因为LiDAR不采集面部图像，仅输出点云，不泄露个人身份信息。\n- 与现有数据集相比，该数据集弥补了室内3D人体点云数据缺乏的空白，同时提供时空序列数据。\n- 数据集类别均衡，避免深度学习模型因类别不平衡而产生偏差，无需额外的重采样处理。\n- 数据预处理可使用min-max归一化，并提供了pcap2pcd转换代码（GitHub和Mendeley Data可获取）。\n- 当前限制包括仅单名男性人才、四种基本姿态、仅在室内收集，未来可扩展至多样人群和多姿态以增加多样性。\n- 该数据集可与其他传感器（如毫米波雷达、摄像头）融合，支持多模态计算机视觉和深度学习研究。"
}