# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
###### Abstract
One of the application areas for this technology, body size measurement, plays a crucial role in various fields such as the apparel industry, healthcare, and fitness. Traditionally, body measurements have been carried out in a manual manner, which can be time-consuming and inconvenient for users. Especially when purchasing clothing online, accurately measuring body sizes is essential, but until now, there has been the inconvenience of users having to measure their dimensions using measuring tools themselves. To address these issues, the development of automatic body size measurement technology is necessary. The goal is to develop 3D length measurement technology by utilizing the standard 2D RGB camera and built-in LiDAR sensor of mobile devices to extract depth information alongside 2D images of subjects. Through this, we seek to provide a platform that allows easy measurement of body dimensions in everyday life without the need for specialized 3D equipment. Software utilizing mobile devices can be implemented at a low cost and can significantly enhance user convenience by measuring lengths in real time and providing immediate results. Additionally, an intuitive user interface increases accessibility to the technology and efficiently uses energy and resources, thereby reducing time and energy consumption. Specifically, body size is an important metric in clothing purchases. Previously, users had to measure their dimensions themselves, but the automatic dimension measurement platform proposed in this study supports users in easily measuring their body dimensions, thereby greatly enhancing the convenience of online shopping. This is expected to improve user experience and have positive effects such as reducing return rates. This study makes several key contributions. First, it developed a technology for accurately measuring 3D lengths by utilizing the LiDAR sensor embedded in mobile devices without relying on specialized 3D equipment. Second, it introduced a keypointdetection technique based on the HRNet model and improved measurement accuracy by accurately identifying the positions of body parts through transfer learning. Third, it developed an algorithm that effectively integrates 2D RGB images and 3D depth maps, enabling accurate length measurements in three-dimensional space. Lastly, it implemented an algorithm that automatically measures the circumferences of body parts based on images captured from the front and side, and demonstrated high accuracy through experiments. Meanwhile, there has also been extensive research on estimating body circumference by reconstructing the human body as a 3D mesh. Approaches such as SMPL [9], PIFu [10], and DeepHuman [11] share a similar concept with this study in that they attempt to restore a three-dimensional shape from limited images (or scanned data). However, each model differs in problem setting and application context, requiring specialized equipment, complex computational procedures, or parametric learning based on a naked body model. In this study, we discuss these methods and highlight their differences from our approach more specifically in Section 2.4. This paper is structured as follows. Section 2 reviews related studies and discusses the distinctions from the current research. Section 3 provides a detailed explanation of the principles behind storing 3D depth information using a mobile phone LiDAR sensor. Section 4 introduces the proposed methodology, detailing the processes of data collection, preprocessing, keypoint detection and merging, and the body circumference measurement algorithm. Section 5 presents the experimental setup and results, validating the effectiveness of the proposed method through result analysis. Finally, Section 6 discusses the limitations of this study and outlines directions for future research.