# 3D Point Cloud Dataset of Heavy Construction Equipment
## 2 Related Works  
### Review of Classification and Segmentation Dataset in the Construction Field  
Most recently released classification and segmentation datasets for heavy construction equipment are primarily constructed in 2D images, such as ImageNet [29], the AIM (Advanced Infrastructure Management group (Lake Ariel, PA, USA)) dataset [30,31], the ACID (Alberta Construction Image Dataset) [32], and the Microsoft COCO (Common Objects in Context) dataset [33]. Additionally, research institutions often construct their own data for conducting research [23,34] by collecting substantial image datasets from various sources, including online and offline environments such as construction sites. Consequently, in 2D image-based ML and DL research in the construction industry, there are various methods to easily acquire the necessary datasets for training and testing.  
However, finding well-established benchmark 3D point cloud-based heavy construction equipment datasets for classification and segmentation research remains a challenge. Therefore, previous research in object recognition for heavy construction equipment, based on 3D point cloud data, often involved generating the point cloud data for heavy construction equipment from CAD files or scanning heavy construction equipment using methods such as TLS or UAV to perform model training and testing [35,36]. Although not specific to heavy construction equipment, research has been conducted with datasets involving large-scale bridge components such as piers, slabs, abutments, and girders; and semantic segmentation research based on 3D point cloud data [37]. Additionally, datasets based on point cloud data were created for scaffolds in construction sites to facilitate semantic segmentation research using 3D point cloud data [38]. In addition to research in the construction field that involves 3D scanning of large-scale objects using TLS or UAV, research is also being conducted on 3D scanning of diecast models such as architectural design models, cultural heritages, and rendering models of automobile designs, to extract 3D point cloud data and construct datasets [39,40].  
Furthermore, utilizing pseudo-Lidar to execute classification based on 3D point cloud data of heavy construction equipment [39], or conducting classification research through the self-constructed 2D image dataset for steel plate surface detection [40] demonstrates innovative approaches to leveraging specialized datasets for advancing construction equipment. As discussed above, there are various well-established (benchmark) datasets available for 2D image-based heavy construction equipment. Conversely, finding representative datasets based on 3D point cloud data for heavy construction equipment, construction materials, workforce, and other related aspects of the construction industry is not an easy task. The absence of such representative training and testing datasets for ML and DL in the construction industry affects research efficiency from a broader perspective within the construction industry [30,32,41,42]. Furthermore, individual research institutions require significant amounts of time and effort to independently construct high-quality 3D heavy construction equipment point cloud data for ML and DL research.