# 3D Point Cloud Dataset of Heavy Construction Equipment
###### Abstract  
Object recognition algorithms and datasets based on point cloud data have been mainly designed for autonomous vehicles. When applied to the construction industry, they face challenges due to the origin of point cloud data from large earthwork sites, resulting in high volumes of data and density. This research prioritized the development of 3D point cloud datasets specifically for heavy construction equipment, including dump trucks, rollers, graders, excavators, and dozens; all of which are extensively used in earthwork sites. The aim was to enhance the efficiency and productivity of machine learning (ML) and deep learning (DL) research that relies on 3D point cloud data in the construction industry. Notably, unlike conventional approaches to acquiring point cloud data using UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), the datasets for the five types of heavy construction equipment established in this research were generated using 3D-scanned diecast models of heavy construction equipment to create point cloud data. 3D point cloud data; heavy construction equipment; large-scale benchmark dataset; 3D laser scanning; classification; segmentation +
Footnote †: journal: Applied  
csc  
collection equipment such as Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) equipped with a variety of sensors--including 2D image cameras, depth cameras, and 2D and 3D LiDAR--has made it easier to obtain terrain data in construction sites. The availability of such technologies allows the application of object recognition technology in construction [21; 22]. The combined development of technologies such as AI and data collection sensors and equipment has provided a solid foundation for active research into utilizing AI in construction. To date, AI research in the construction industry has primarily centered around computer vision technology, including 2D images and videos. This research has focused on construction safety and process monitoring, as well as concrete damage measurement and assessment [23]. Recent research has focused on object detection and tracking, utilizing images and videos for evaluating the use of safety gear, including helmets and safety belts [24]; for monitoring construction workers, the general public, and forklifts [25]; recognizing cracks in concrete structures such as roads, buildings, and bridges; and assessing potential hazards [26]. While 2D image-based object detection research shows excellent accuracy and speed, it faces challenges when capturing images in environments where 2D cameras are susceptible to changing lighting conditions, particularly in areas without direct sunlight [15]. Consequently, there has been a growing trend in the construction industry to utilize 3D Lidar sensors to overcome the limitations of data collection sensors. This is because 3D Lidar sensors are known for their ability to collect data without being affected by lighting conditions, making them suitable for construction environments exposed to varying conditions [15]. Furthermore, data collected in construction sites, especially data obtained with 2D cameras and videos, may lack Z-coordinate values due to the sensor's characteristics, introducing errors in Z-values. In contrast, 3D Lidar sensors collect 3D point cloud data, including accurate measurements of X, Y, and Z coordinates. This in turn makes them more suitable for the construction industry, which requires accuracy [27]. However, despite these advantages, there are currently no representative 3D point cloud data-based object recognition datasets available for application in construction sites. Research institutions have been individually creating datasets due to the lack of such resources. Constructing 3D point cloud data for construction sites requires significantly more time and computing resources compared to building 2D image datasets. Construction sites are primarily outdoors and involve large-scale structures such as roads, bridges, and buildings.