# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 2 Mobile Device 3D Measurement Technology
### Limitations of Existing Studies and Differentiation of This Study  
Recent studies have reported attempts to leverage 3D data acquired from LiDAR sensors for body circumference measurement. However, despite the active investigation of 3D mesh reconstruction-based methods for human body measurements, models such as SMPL, PIFu, and DeepHuman still face structural limitations for immediate circumference measurement in mobile environments. SMPL, for instance, is a parametric model trained on nude body shapes, making it difficult to fully account for variations in body shape and circumference when clothing is involved. Additionally, estimating SMPL parameters (e.g., \(\beta\), \(\theta\)) from a single image demands a complex fitting procedure requiring a high-performance GPU, rendering real-time application practically infeasible.  
Meanwhile, PIFu can reconstruct high-resolution 3D surfaces using implicit functions such as capable of capturing fine details such as clothing wrinkles, but accurately estimating the rear or occluded regions from a single viewpoint remains difficult, and the method typically requires multi-view acquisition and substantial computational resources, making it unsuitable for typical mobile devices. Finally, DeepHuman employs a voxel-based 3D reconstruction approach, where increasing resolution leads to exponential growth in GPU memory usage and computational overhead. Moreover, because the model must infer unobservable areas from a single viewpoint, the margin of error increases. Ultimately, without multi-view input, achieving sufficient accuracy is challenging, and near-real-time operation on mobile devices also remains limited.  
To overcome the computational and practical constraints of these 3D mesh reconstruction approaches, this study proposes a lightweight method that combines 2D keypoint detection with LiDAR-sensor data. Specifically, we rapidly identify (target) the measurement regions using a high-resolution 2D pose estimation network, such as HRNet, and then compute only the simple Euclidean distance in 3D space. This method eliminates the need for complex 3D mesh reconstruction while still leveraging depth data, allowing for accurate circumference measurements with lower computational cost. As a result, it provides sufficiently accurate measurements from a single viewpoint and can operate in near-real time on mobile devices without the need for high-performance GPU or multi-view data.  
Experimentally, we validated our algorithm's performance using uniform 3D data such as cylindrical objects and mannequins, demonstrating high accuracy and practical utility across various body regions. In particular, on a system equipped with an AMD Ryzen 5950X (16 cores/32 threads), 128 GB of RAM, two RTX 3090 24 GB GPUs, and a 1 TB M.2 SSD, keypoint detection and circumference measurement took an average of 0.7 s, indicating near-real-time processing capability. This underscores the advantage of our lightweight approach, which relies solely on CPU-based Euclidean distance computation following 2D keypoint detection, eliminating the need to reconstruct the entire 3D space.