Geometry aware method improves 3d scans of fragile thin heritage artifacts
Geometry-Constrained Bidirectional Point Cloud Registration for Thin, Sheet-Like Heritage Artifacts
Computer Vision and Pattern Recognition
Summary
Scanning fragile, thin historical objects is hard because you can't mark or handle them much, and you need to scan both sides separately. The authors designed a new way to match the front and back 3D scans by using their thickness and shape to avoid errors like flipping one side or unstable fits. Their approach checks different rotations and uses the estimated thickness to keep the scanned parts aligned correctly. This helps create accurate 3D models of delicate cultural items without touching or damaging them.
What this means in practice
- •For cultural heritage conservators: Register front and back 3D scans of fragile artifacts without physical markers to create accurate digital records.
- •For industrial metrology teams: Improve alignment of thin sheet metal or material scans where thickness constraints help avoid misregistration errors.
Authors
Yuezhe Zhang, Lei Wei, Jingnan Du, Shuai Wan
Abstract
Non-contact three-dimensional reconstruction of thin, sheet-like heritage artifacts poses significant geometric and registration challenges. Due to their fragility, these artifacts cannot be suspended or equipped with artificial markers, necessitating independent acquisition of their front and back surfaces. Subsequent registration proves difficult due to the limited number of shared geometric features and the scarcity of explicit physical constraints, which may result in rotational ambiguity, instability, and structural collapse during iterative optimization. To address these challenges, we propose a geometry-constrained bidirectional point cloud registration method specifically tailored for thin, sheet-like heritage artifacts. The method integrates semantic-guided preprocessing, Principal Component Analysis (PCA)-based geometric normalization, and a thickness-aware registration strategy. The estimated physical thickness is incorporated as a geometric constraint to preserve structural integrity during registration. Rotational ambiguity is resolved by evaluating a finite set of global rotation hypotheses, each refined using the point-to-plane Iterative Closest Point (ICP) algorithm, with the optimal transformation selected via a geometry-aware fitness criterion consistent with the thickness scale. Experimental results show that the proposed method achieves competitive or improved performance in most cases, particularly in projected area consistency and physically plausible front-back alignment. In addition, the thickness-aware constraint and rotation hypothesis evaluation reduce the risk of degenerate configurations in which the two surfaces are incorrectly flipped while still yielding deceptively acceptable numerical scores, supporting reliable non-contact digitization of delicate and thin heritage artifacts. Implementation details are available at https://zyz-nwpu.github.io/GCBPCR/.