# 3D Point Cloud Dataset of Heavy Construction Equipment
## 3 Development of 3D-ConHE Dataset
### Preprocessing of 3D Point Cloud Data  
In the preprocessing phase, the scanned ASC files were converted to STL and PCD file formats. Point cloud density calculations and adjustments, as well as removal of the ground plane from the PCD files, were performed; as shown in Figure 5. Open3D library [51] was used for file conversion and point density adjustment in this preprocessing stage, and Cloudcompare 2.6.3 [Windows 64 bits] [52] was used for scale adjustments and ground plane removal. * Converting Files: To generate PCD files from the ASC files produced during the scanning process, the ASC files for each piece of equipment were merged to create mesh files in STL format. These mesh files were further processed to obtain the final PCD files through PLY files. The 3D scanner's software ( EinScan SE/SP Software Version 3.1.3.0) facilitated the conversion of ASC, STL, and PLY files. Open3D was then used to convert PLY files into PCD files. This conversion process yielded a total of 669 mesh-converted PLY files and 669 PCD files; all consistent with the scale of the diecast models of heavy construction equipment. * Adjusting Scale: The diecast models of heavy construction equipment used for 3D scanning were scaled down to between 1:32 and 1:87, compared to their real-size counterparts. To facilitate the use of these differently scaled models in this research, a scale adjustment process was executed using the Cloudcompare software. This ensured that the scanned data created in this research matched the actual scale of heavy construction equipment used on-site. * Removing Ground Plane: After the scale adjustment, the ground planes in the PCD files of the diecast models of heavy construction equipment were removed using the Cloudcompare software. This phase was taken into account, considering that the point cloud data of a ground plane is not captured in actual data collected using UAV and UGV equipment. The aim was to make the data more closely resemble on-site data obtained through scanning, thus enhancing the accuracy of ML and DL object detection models. This operation led to a reduction in the size of the 3D-ConHE dataset. * Adjusting Point Density: This research examined the point cloud data of heavy construction equipment collected by UAV to adjust the point cloud data's point density. Typically, the point cloud data of heavy construction equipment captured by UAV, as displayed in Figure 6, features significantly lower point density compared to data generated from scanning diecast models. This discrepancy arises from the equipment's characteristics and the data capture method. In Figure 6, the data were acquired using a DJI PHANTOM 4 RTK (Shenzhen, China), equipped with a 20-megapixel camera, flying at an altitude of 100 m. Figure 6a shows the extracted point cloud data from heavy construction equipment on a construction site captured by the UAV, while Figure 6b displays data generated from scanning heavy construction equipment diecast models indoors. When comparing the point density between Figure 6a,b, the lower point density in Figure 6a is likely attributed to the drone capturing data from an altitude of 100 m above the ground. Furthermore, a drone capturing data at a 100 m altitude can be affected by wind, causing it to fluctuate in altitude, sometimes ranging between 80 and 120 m above the ground. The unreliability of this on-site data collection method results in the point cloud data captured by UAVs having lower point densities than data generated from static scanning of diecast models indoors. Consequently, this research employed point density values at a UAV data capture  
Figure 5: Data preprocessing process. height of 100 m as reference values for diecast models (Figure 6d). Additionally, data captured at altitudes of 80 and 120 m were taken into account to adjust the point density of diecast models, as depicted in Figure 6d,e; introducing a variance of +20% and \(-\)20%.