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

2026-04-21

AI Summary

This work addresses the challenges of point cloud to 3D model registration in real-world scenarios—such as sparsity, noise, occlusion, and clutter—by introducing PC2Model, the first publicly available benchmark dataset that integrates both synthetic and real-world scanned data. Combining LiDAR and structured-light scanning technologies, the dataset provides a testbed featuring precise ground-truth alignments alongside realistic disturbances, adhering to the standards of the ISPRS ICWG II/Ib working group. By enabling unified evaluation of both classical and data-driven registration methods, this benchmark facilitates systematic analysis of cross-domain generalization and transfer capabilities, thereby advancing the deployment of robust registration algorithms in applications such as building monitoring and autonomous driving.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

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/uploads/17581812.

Problem

• point cloud registration
• 3D model alignment
• sparsity
• occlusion
• domain transferability

Innovation

• point cloud registration
• 3D model alignment
• sim-to-real transfer
• benchmark dataset
• PC2Model