Point cloud registration involves aligning one point cloud with another or with a three-dimensional (3D) model, enabling the integration of multimodal data into a unified representation. This is essential in applications such as construction monitoring, autonomous driving, robotics, and virtual or augmented reality (VR/AR). With the increasing accessibility of point cloud acquisition technologies, such as Light Detection and Ranging (LiDAR) and structured light scanning, along with recent advances in deep learning, the research focus has increasingly shifted towards downstream tasks, particularly point cloud-to-model (PC2Model) registration. While data-driven methods aim to automate this process, they struggle with sparsity, noise, clutter, and occlusions in real-world scans, which limit their performance. To address these challenges, this paper introduces the PC2Model benchmark, a publicly available dataset designed to support the training and evaluation of both classical and data-driven methods. Developed under the leadership of ICWG II/Ib, the PC2Model benchmark adopts a hybrid design that combines simulated point clouds with, in some cases, real-world scans and their corresponding 3D models. Simulated data provide precise ground truth and controlled conditions, while real-world data introduce sensor and environmental artefacts. This design supports robust training and evaluation across domains and enables the systematic analysis of model transferability from simulated to real-world scenarios. The dataset is publicly accessible at: \href{https://doi.org/10.5281/zenodo.17581812}{https://zenodo.org/records/17581812}

翻译：点云配准涉及将一个点云与另一个点云或三维模型对齐，从而将多模态数据整合为统一表示。这在建筑监测、自动驾驶、机器人技术以及虚拟现实/增强现实（VR/AR）等应用中至关重要。随着激光雷达（LiDAR）和结构光扫描等点云采集技术的日益普及，以及深度学习的最新进展，研究重点已逐渐转向下游任务，特别是点云到模型（PC2Model）配准。尽管数据驱动方法旨在实现该过程的自动化，但真实扫描中的稀疏性、噪声、杂乱和遮挡问题限制了其性能。为应对这些挑战，本文介绍了PC2Model基准——一个旨在支持经典方法和数据驱动方法训练与评估的公开数据集。该基准由ICWG II/Ib工作组牵头开发，采用混合设计，将模拟点云与部分情况下的真实扫描及其对应三维模型相结合。模拟数据提供精确的地面真值和受控条件，而真实数据则引入传感器和环境伪影。该设计支持跨领域的稳健训练与评估，并能系统分析模型从模拟场景到真实场景的可迁移性。该数据集公开访问链接为：\href{https://doi.org/10.5281/zenodo.17581812}{https://zenodo.org/records/17581812}