## 6 Conclusions  
Although there have been remarkable advances made in artificial intelligence technology, it was developed with a focus on specialized algorithms that are still highly dependent on the format of the data. Despite the development and widespread use of devices, this aspect makes it difficult to converge and utilize the capabilities of different data, thus the applicability to other fields is low. Our study shows that even a simple connection between a deep learning model and point cloud data can exceed the performance of previous sizing methods that required delicately controlled environments and algorithms. Additionally, this method exploits the capabilities of a device that is readily available and it easy to use in various small businesses. In our approach, the point cloud is simply used to find the distance between two points, but the size measurement method using the point cloud has greater potential for advancement. A point cloud has many points projected at a uniform angle; therefore, there can be more points between the sizing points. It is possible to find not only the distance between two distant points, but also the distance between all adjacent points, and the size can be obtained by summing all these distances. A garment has different curves that are not flat; therefore, we can estimate a more precise size measurement by segmentalizing the arch shapes. In this manner, even a slight wrinkle or clothing hanging on a hanger can be sized by calculating the distance to the surface. We believe that this approach will allow size measurements in various situations. We will elaborate on this method in a future study that is currently in progress. Conceptualization, S.K. ([PERSON]) and S.K. ([PERSON]); methodology, S.K. ([PERSON]); software, S.K. ([PERSON]); validation, S.K. ([PERSON]); formal analysis, S.K. ([PERSON]) and S.K. ([PERSON]); investigation, S.K. ([PERSON]); resources, S.K. ([PERSON]); data curation, S.K. ([PERSON]) and H.M.; writing--original draft  
Figure 2: Some examples of size measurement results. These results show the robustness of the point detector model to the shape, orientation, color, and background. The left arm of the long sleeve top (2, 7) and left leg of the shorts (3, 8) were folded, and the skirt (10) had a different front/back shape. The trousers (4, 9) rotated 90\({}^{\circ}\) counterclockwise and the white long sleeve top (2) were the same color as the background; all parts were successfully sized. preparation, S.K. ([PERSON]) and J.O. ; writing--review and editing, S.K. ([PERSON]), J.O., Y.L. and H.K. ; visualization, S.K. ([PERSON]) and H.M.; supervision, S.K. ([PERSON]); project administration, S.K. ([PERSON]); funding acquisition, S.K. ([PERSON]) All authors have read and agreed to the published version of the manuscript. This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (NRF-2021R1A4A5032622 and NRF-2020R1C1C1A01005229). Not applicable. All source codes and the datasets for testing are available online at [[https://github.com/ZinoStudio931/Automatic-Garments-Size-Measurement-using-HRNet-and-Point-Cloud](https://github.com/ZinoStudio931/Automatic-Garments-Size-Measurement-using-HRNet-and-Point-Cloud)]([https://github.com/ZinoStudio931/Automatic-Garments-Size-Measurement-using-HRNet-and-Point-Cloud](https://github.com/ZinoStudio931/Automatic-Garments-Size-Measurement-using-HRNet-and-Point-Cloud)) (generated on 26 April 2022) and [[http://www.hifai.pe.kr/](http://www.hifai.pe.kr/)]([http://www.hifai.pe.kr/](http://www.hifai.pe.kr/)) (generated on 20 April 2022), respectively. The DeepFashion2 dataset is available at [[https://github.com/switchablenorms/DeepFashion2](https://github.com/switchablenorms/DeepFashion2)]([https://github.com/switchablenorms/DeepFashion2](https://github.com/switchablenorms/DeepFashion2)) (accessed on 30 November 2021) upon request. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: