###### Abstract  
Automatic garment size measurement approaches using computer vision algorithms have been attempted in various ways, but there are still many limitations to overcome. One limitation is that the process involves 2D images, which results in constraints in the process of determining the actual distance between the estimated points. To solve this problem, in this paper, we propose an automated method for measuring garment sizes using computer vision deep learning models and point cloud data. In the proposed method, a deep learning-based keypoint estimation model is first used to capture the clothing size measurement points from 2D images. Then, point cloud data from a LiDAR sensor are used to provide real-world distance information to calculate the actual clothing sizes. As the proposed method uses a mobile device equipped with a LiDAR sensor and camera, it is also more easily configurable than extant methods, which have varied constraints. Experimental results show that our method is not only precise but also robust in measuring the size regardless of the shape, direction, or design of the clothes in two different environments, with 1.59% and 2.08% of the average relative error, respectively. garment measurement; LiDAR, point cloud data; deep leaning; convolutional neural networks; keypoint estimation +
Footnote †: journal: Applied Sciences  
0000-0001-9993-3227]Geon-Michael-M  
points and to calculate the distance between the points, respectively. As shown in Figure 1, the proposed method consists of three steps. First, we capture clothes lying flat on the floor using a mobile device equipped with a camera and a LiDAR sensor. With a single capture, a dataset consisting of an image, depth map, and point cloud data are created, as shown in Figure 1a. Next, the deep learning-based point detector detects the locations of the size measurement points corresponding to the clothing categories in the 2D image, as exhibited in Figure 1b. A computer vision deep learning model is used as a measurement point detector to determine the pixel coordinates of the multiple measurement points. Finally, we calculate the sizes based on the depth information of the point cloud. The depth map represents the depth information of each point, which is in the 2D coordinates, the same as the 2D image. As illustrated in Figure 1c, each point that is detected in the previous step is mapped to a point in the point cloud and assigned the distance from the LiDAR sensor. Based on the actual distance information between the device and the scanned space, we can find the actual sizes using the depths and contained angle. Our method succeeds in measuring the clothing size (as precise as manual sizing) by simply combining the advantages of the deep learning model and point cloud data. Section 2 introduces the deep learning algorithms our team apply and how we utilize the advantages of LiDAR sensors to minimize intervention from human factors. We also present the details of the proposed method in Section 3 and outline the conducted experiments to verify the method's accuracy, tolerance, and robustness in Section 4. Finally, the benefits of the suggested procedure and further studies are summarized in Sections 5 and 6, respectively. Overall, our contributions are as follows:  
* We introduce a novel method for automating garment sizing using a mobile device with cameras and LiDAR sensors without environmental controls. * We illustrate how we can successfully measure sizes by exploiting 3D point cloud features, as precise as manual measurements. * We present how robust the proposed method is by examining experimental results (with and without backgrounds) using deep learning-based point detectors. Figure 1: Three steps of the automatic garment size measurement approach. (**a**) We used a mobile device equipped with cameras and a LiDAR sensor to collect images and point clouds of clothes taken from the same viewpoint. (**b**) The deep learning-based point detector detects the two-dimensional coordinates in the picture of the measurement points.