# 3D Point Cloud Dataset of Heavy Construction Equipment
## 4 Conclusions  
The KITTI dataset, which is a representative dataset for autonomous vehicles, was introduced in 2012 and has been leading research in areas such as 3D object detection and 3D tracking for autonomous vehicles. It continues to be actively utilized in various studies even in recent times [49]. Furthermore, globally, the KITTI dataset significantly influences the development of datasets for artificial intelligence research [50]. Thus, in the construction industry as well, the development of datasets based on 3D point cloud data, similar to the KITTI dataset, is necessary for advancing AI research.  
For this reason, our research has developed the 3D-ConHE dataset, focusing on five types of heavy construction equipment, and made it publicly available to advance related research. Our dataset enhanced efficiency by scanning diecast models of the heavy construction equipment. Furthermore, it was scaled to match the actual size of the equipment used in the field, scanned considering the motion joint movement directions, and constructed with three different point cloud densities, taking into account the point  
Figure 8: Comprehensive configuration of the 3D-ConHE dataset.  
cloud density of the equipment captured by UAV. By considering and incorporating these various conditions, we were able to increase the utility of our dataset.  
This study, notwithstanding the advantages previously described, has certain limitations. Primarily, although the 3D-ConHE dataset was developed, it was not verified and validated through field experiments. Future research is planned to verify and validate the dataset developed in this study. Additionally, the 3D-ConHE dataset created in this research does not encompass the entire range of heavy construction equipment used in the construction industry but is limited to five types of heavy construction equipment due to the nature of the research. This limitation in the scope of the dataset represents a constraint of the current study. Consequently, plans are in place to progressively increase the variety of heavy construction equipment included in the 3D-ConHE dataset in future studies.  
Despite these limitations of the 3D-ConHE dataset, our dataset is anticipated to be useful for research in areas such as classification, part segmentation, and semantic segmentation related to heavy construction equipment. Furthermore, it is expected to serve as a key technology for autonomous driving in the development of unmanned heavy construction equipment, in conjunction with Machine Guidance (MG) and Machine Control (MC) systems that are actively implemented in the construction industry. We anticipate that our 3D-ConHE dataset will be extensively utilized in AI research based on point cloud data within the construction industry in the future.  
Literature review, S.P.; writing--original draft preparation, S.P.; proposal of the overall framework and direction of the research, S.K.; writing--review and editing, S.K.; dataset construction S.P.; dataset review, S.P. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in [[http://rb.gy/sli2](http://rb.gy/sli2) mw/]([http://rb.gy/sli2](http://rb.gy/sli2) mw/) (accessed on 18 March 2024).  
This research was conducted with the support of the \"National R&D Project for Smart Construction Technology (No. RS-2020-KA158708)\" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.  
The authors declare no conflicts of interest.