PC2Model: ISPRS benchmark on 3D point cloud to model registration

TL;DR

This paper introduces the PC2Model benchmark dataset for evaluating point cloud to 3D model registration methods, combining simulated and real-world data to improve robustness and transferability.

Contribution

It provides a hybrid dataset supporting the training and evaluation of classical and data-driven registration methods, addressing real-world challenges like noise and occlusions.

Findings

Benchmark facilitates systematic analysis of transferability from simulated to real data.

Dataset supports both classical and deep learning registration approaches.

Publicly available at https://zenodo.org/records/17581812.

Abstract

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: https://zenodo.org/records/17581812