Abstract
Accurate 3D scene interpretation in active construction sites is essential for progress monitoring, safety assessment, and digital twin development. LiDAR is widely used in construction because it offers advantages over camera-based systems, performing reliably in cluttered and dynamically changing conditions. Yet most public datasets for 3D perception are derived from densely fused scans with uniform sampling and complete visibility, conditions that do not reflect real construction sites. Field data are often collected as isolated single-station LiDAR views, constrained by safety requirements, limited access, and ongoing operations. These factors lead to radial density decay, fragmented geometry, and view-dependent visibility-characteristics that remain underrepresented in existing datasets. This paper presents SIP, Site in Pieces, a dataset created to reflect the practical constraints of LiDAR acquisition during construction. SIP provides indoor and outdoor scenes captured with a terrestrial LiDAR scanner and annotated at the point level using a taxonomy tailored to construction environments: A. Built Environment, B. Construction Operations, and C. Site Surroundings. The dataset includes both structural components and slender temporary objects such as scaffolding, MEP piping, and scissor lifts, where sparsity caused by occlusion and fragmented geometry make segmentation particularly challenging. The scanning protocol, annotation workflow, and quality control procedures establish a consistent foundation for the dataset. SIP is openly available with a supporting Git repository, offering adaptable class configurations that streamline adoption within modern 3D deep learning frameworks. By providing field data that retain real-world sensing characteristics, SIP enables robust benchmarking and contributes to advancing construction-oriented 3D vision tasks.
Abstract (translated)
在活跃的建筑工地中，对3D场景进行准确解释对于进度监控、安全评估和数字孪生开发至关重要。由于激光雷达（LiDAR）能够在混乱且动态变化的条件下可靠运行，它在建筑业中的应用十分广泛，而相机系统则难以满足这些条件的要求。然而，大多数用于三维感知的公开数据集都是从密集融合扫描中获得，具有均匀采样和完全可见度的特点，这并不反映真实的建筑工地情况。现场数据通常以单独的单站LiDAR视图形式收集，受安全要求、访问限制以及正在进行的操作影响。这些因素导致了径向密度衰减、几何碎片化及视点依赖性可见性的特点，在现有的数据集中显得不足。 本文介绍了一个名为SIP（Site in Pieces）的数据集，该数据集旨在反映在建筑工地中进行LiDAR采集的实际条件约束。SIP提供了使用地面激光雷达扫描仪捕捉的室内和室外场景，并且根据建筑环境的特点进行了点级别的标注：A. 建筑环境；B. 施工操作；C. 工地周围环境。该数据集包括结构组件以及细长临时对象，如脚手架、机电管道和剪式升降机，这些对象由于遮挡造成的稀疏性和几何碎片化使得分割变得特别具有挑战性。 SIP的扫描协议、标注工作流程及质量控制程序为数据集建立了一致的基础。该数据集以开源形式提供，并配有支持性的Git仓库，提供了可适应的类别配置，简化了其在现代3D深度学习框架中的应用。通过提供保留真实世界传感特性的现场数据，SIP能够实现稳健基准测试并推进面向建筑领域的三维视觉任务的发展。
URL
https://arxiv.org/abs/2512.09062
