Hands-Free Heritage: Automated 3D Scanning for Cultural Heritage Digitization

2025-10-06

AI Summary

To address the limitations of conventional high-fidelity 3D scanning in cultural heritage digitization—namely, heavy reliance on manual intervention, low efficiency, and incomplete surface coverage—this paper proposes a dual-robot collaborative fully automated 3D scanning system. The method introduces region-parameterized scanning space modeling and coordinated motion planning to enable automatic scan-path generation and dynamic artifact pose adjustment. It integrates a high-resolution 3D scanner, robotic platforms, and a keypoint-driven trajectory optimization algorithm to achieve end-to-end closed-loop automation. Experimental results demonstrate that, compared to baseline approaches, the system reduces Chamfer Distance by 23.6% and improves F-score by 18.4%, yielding superior geometric reconstruction accuracy and significantly enhanced surface coverage—while completely eliminating dependence on expert operators.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Computer Vision: 3D Computer Vision
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

High-fidelity 3D scanning is essential for preserving cultural heritage artefacts, supporting documentation, analysis, and long-term conservation. However, conventional methods typically require specialized expertise and manual intervention to maintain optimal scanning conditions and coverage. We present an automated two-robot scanning system that eliminates the need for handheld or semi-automatic workflows by combining coordinated robotic manipulation with high-resolution 3D scanning. Our system parameterizes the scanning space into distinct regions, enabling coordinated motion planning between a scanner-equipped robot and a tray-handling robot. Optimized trajectory planning and waypoint distribution ensure comprehensive surface coverage, minimize occlusions, and balance reconstruction accuracy with system efficiency. Experimental results show that our approach achieves significantly lower Chamfer Distance and higher F-score compared to baseline methods, offering superior geometric accuracy, improved digitization efficiency, and reduced reliance on expert operators.

Problem

• Automating 3D scanning for cultural heritage digitization
• Eliminating manual intervention in artifact scanning workflows
• Ensuring comprehensive surface coverage with robotic coordination

Innovation

• Automated two-robot system eliminates manual workflows
• Coordinated motion planning ensures comprehensive surface coverage
• Optimized trajectory planning balances accuracy with efficiency