## 5 Discussion  
Our proposed method exhibited precision and tolerance performance comparable to that of manual sizing, as presented in Table 2 above. In addition, it had practical and robust \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Clothing**} & \multicolumn{2}{c}{**Manual Measurement**} & \multicolumn{2}{c}{**Exp. 1: White Background**} & \multicolumn{2}{c}{**Exp. 2: Raw Floor**} \\ \cline{2-6}  & \multirow{2}{*}{**Abs. Err.**} & \multirow{2}{*}{**Rel. Err.**} & **Abs. Err.** & **Rel. Err.** & **Abs. Err.** & **Rel. Err.** \\  & & & **(S.E.)** & **(S.E.)** & **(S.E.)** & **(S.E.)** \\ \hline Short & \multirow{2}{*}{1.4000} & \multirow{2}{*}{0.0309} & 0.5000 & 0.0126 & 0.5552 & 0.0143 \\ sleeve top & & & (0.0376) & (0.0009) & (0.0424) & (0.0011) \\ \hline Long & \multirow{2}{*}{1.3417} & \multirow{2}{*}{0.0412} & 0.6001 & 0.0180 & 0.6229 & 0.0197 \\ sleeve top & & & (0.0546) & (0.0017) & (0.0700) & (0.0022) \\ \hline Shorts & \multirow{2}{*}{0.9524} & \multirow{2}{*}{0.0254} & 0.5041 & 0.0143 & 0.6139 & 0.0168 \\  & & & (0.0461) & (0.0013) & (0.0525) & (0.0015) \\ \hline Trousers & \multirow{2}{*}{1.3150} & \multirow{2}{*}{0.0366} & 0.6571 & 0.0195 & 1.2123 & 0.0357 \\  & & & (0.0728) & (0.0021) & (0.1038) & (0.0031) \\ \hline Skirts & \multirow{2}{*}{1.4583} & \multirow{2}{*}{0.0285} & 0.5511 & 0.0114 & 0.6784 & 0.0131 \\  & & & (0.0492) & (0.0009) & (0.0648) & (0.0013) \\ \hline Total & \multirow{2}{*}{1.2935} & \multirow{2}{*}{(c) 0.0325} & 0.5707 & (a) 0.0159 & 0.7371 & (b) 0.0208 \\  & & & (0.0529) & (0.0015) & (0.0680) & (0.0019) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results. ‘Abs. Err.’, ‘Rel. Err.’, and ‘S.E’ are abbreviations for absolute error, relative error, and standard error, respectively. In experiments 1 and 2, the relative errors were (a) 1.59% and (b) 2.08%, respectively, lower than the (c) 3.25% of the manual measurement. advantages over the previous methods [8, 9] that used only 2D images in two respects: (1) no external workbench constraints and (2) direct sizing point detection. One of the major benefits of using a LiDAR sensor to measure clothing size is that it does not require any environmental controls, as described in Table 3. Because past methods [8, 9, 10, 11] relied solely on 2D images, they required delicate environment controls and camera calibration to map the spatial information for each pixel. For these approaches, the external environment has to be designed by considering all components (e.g., the distance between the clothes and the camera, the angle and curvature of the camera lens, the lighting plate under the clothes, etc.) related to the clothing and the camera. In other words, they require allotted space for equipment installation and preparatory work, which is impractical to apply in small businesses and with consumers. By contrast, the proposed method technically does not require these constraints. The operator who wants to measure the sizes does not need to know about the information, such as camera specifications or distance to the clothes. It is because the LiDAR sensor can record the distance to the target space at the level of 0.00001 cm with the same viewpoint as the 2D image. In summary, with a single mobile device, anyone, anywhere, can measure size instantly. A point detector based on vision deep learning is robust to shape, orientation, and color [35] because it finds the size measurement point directly without a contour extraction process. As shown in Figure 2, we succeeded in measuring the sizes of clothes of various colors and designs in an environment with and without a background. Moreover, our  
\begin{table}
\begin{tabular}{c c c} \hline \hline  
experimental results prove its tolerance to errors that may be caused by human factors. In the fashion industry, where designs are diverse, it is difficult to fit multiple clothing images on a predefined template.