3D human pose point cloud data of light detection and ranging (LiDAR)

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-09-10 DOI:10.1016/j.dib.2025.112043

Farah Zakiyah Rahmanti , Moch. Iskandar Riansyah , Oddy Virgantara Putra , Eko Mulyanto Yuniarno , Mauridhi Hery Purnomo

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引用次数: 0

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.

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光探测与测距（LiDAR）三维人体姿态点云数据

三维光探测和测距（LiDAR）传感器与计算机视觉和深度学习密切相关。3D激光雷达传感器通常嵌入在智能车辆中，用于分割人、汽车、卡车、电机和其他物体。然而，3D激光雷达也可以在室内使用，以预测对个人隐私更友好的人体姿势，因为3D激光雷达不捕获面部图像，但它以点云的形式产生数据。点云产生空间、几何和时间信息，可用于预测、检测和分类人体姿势和活动。3D激光雷达的数据输出包括空间和时间数据，格式为PCAP （. PCAP）和JSON （. JSON）。PCAP文件包含3D人体姿态点云的序列帧，JSON文件包含元数据。每个人体姿势类标签有一个PCAP文件和一个JSON文件。原始时空数据必须处理成PCD格式，作为每个人体姿态的三维人体姿态点云数据集。总的人体姿态数据集是1400个用于深度学习训练和测试过程的PCD格式（. PCD）的三维点云数据，由四个人体姿态标签组成。标签类是手侧向，坐下，蹲下来和站立的人体姿势，每个类都有280个3D点云数据用作训练数据。而测试数据为280个三维点云数据。数据收集过程使用3D激光雷达，三脚架，个人电脑/笔记本电脑和人才，展示基本的人体姿势。使用的3D LiDAR是OS1， Ouster的产品，范围90-200米，128通道分辨率，温度-40 - 60°c。对于人才，本次拍摄中有一人，性别为男性。然而，在其发展过程中，它也可以利用女性或儿童或老年人的天赋来丰富人体姿势数据集。人才年龄在30到40岁之间。3D激光雷达与人才位置的距离为120cm。数据收集于上午10时至下午1时进行。在室内。该数据集用于使用深度学习算法之一卷积神经网络（CNN）进行人体姿势预测。然而，开发人员也可以使用其他深度学习算法，如变压器、图神经网络（GNN）等。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.