# 3D Point Cloud Dataset of Heavy Construction Equipment
## 2 Related Works
### Review of Large-Scale 3D Point Clouds Datasets for Classification and Segmentation  
Currently, the benchmark datasets constructed from 3D point cloud data are primarily focused on indoor objects. These datasets include ModelNet40 [43], S3 DIS (Stanford 3D Indoor Scene Dataset) [44], and ShapeNet [45], as shown in Table 1. However, more recently, datasets such as Semantic3D [46], Paris-Lille-3D [47], and Toronto-3D [48]--targeting urban environments--have been developed, enabling large-scale 3D point cloud data for ML and DL research. Such large-scale point cloud data classification and segmentation datasets, in contrast to datasets focused on indoor environments, are constructed by scanning large-scale urban facilities such as churches, streets, villages, soccer fields, and castles, using static Terrestrial Laser Scanning (TLS) and Mobile Laser Scanning (MLS); these share similar characteristics with data from 3D-scanned large-scale construction sites located outdoors. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Name** & **Year** & **Environment** & **Primary Fields** & **File Format** & **Objects/Points** & **Number of Classes** & **Application Technology** & **Data Acquisition Type** \\ \hline ShapeNet & 2015 & Indoor & X, Y, Z, R, G, B &.obj & \begin{tabular}{c} 51,300 objects \\ (ShapeNetCore), \\ 12,000 objects \\ (ShapeNetSem) \\ \end{tabular} & 55 (ShapeNetCore), & Classification & \begin{tabular}{c} Conversion of \\ CAD models \\ \end{tabular} \\ \hline ModelNet & 2015 & Indoor & X, Y, Z, number of vertices, edges, faces &.off & \begin{tabular}{c} 12,311 objects \\ (ModelNet40), \\ 4899 objects \\ (ModelNet10) \\ \end{tabular} & 10 (ModelNet 10), & Classification & \begin{tabular}{c} Conversion of \\ CAD models \\ \end{tabular} \\ \hline SSDIS & 2016 & Indoor & X, Y, Z, R, G, B & hS & 695 million points & 13 & Segmentation & \begin{tabular}{c} Converting CAD files \\ into mesh files \\ \end{tabular} \\ \hline Semantic3D & 2017 & Urban & X, Y, Z, intensity, class &.st & 4 billion points & 8 & Segmentation & TLS \\ \hline Paris-Little-3D & 2018 & Urban & X, Y, Z, intensity, class &.ply & 143 million points & 50 & Segmentation & MLS \\ \hline Toronto-3D & 2020 & Urban & \
\begin{tabular}{c} X, Y, Z, R, G, B, \\ intensity, GPS time, scan angle, label \\ \end{tabular} &
\begin{tabular}{c} \text{ply} \\ \end{tabular} & 78 million points & 8 & Segmentation & MLS \\ \hline Semantic KITTI & 2019 & Urban & X, Y, Z, intensity, label &.bin & 23,201 objects & 28 & Segmentation & MLS \\ \hline
3D-ConHE (ours) & 2023 & Indoor & X, Y, Z, &.st &.ply & 4683 objects & - & Classification, &
\begin{tabular}{c} Portable \\ segmentation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: 3D point clouds-based classification and segmentation benchmark datasets. In addition to the aforementioned datasets, recent developments in large-scale 3D point cloud datasets include the HLS (Helmet Laser Scanning) dataset and the MCD (Multi-Campus Dataset) [49]. The HLS dataset has been constructed by installing multisensors on helmets and applying Simultaneous Localization and Mapping (SLAM) technology in real time to build large-scale 3D datasets targeting forest areas, underground facilities, and infrastructure facilities [49]. Furthermore, the MCD dataset represents the latest in large-scale 3D point cloud datasets, generating and providing three-dimensional point cloud data for semantic segmentation of various campuses by applying SLAM technology [50]. Among these large-scale 3D point clouds datasets, the Semantic3D dataset was the pioneer and it has since been actively employed in classification and semantic segmentation research (Table 1). The Semantic3D dataset consists of eight class labels and over 4 billion points, making it ideal for high-performance large-scale 3D point clouds semantic segmentation tasks.