Geometry-Constrained Bidirectional Point Cloud Registration for Thin, Sheet-Like Heritage Artifacts

📄 arXiv: 2610.07793v1 📥 PDF

作者: Yuezhe Zhang, Lei Wei, Jingnan Du, Shuai Wan

分类: cs.CV

发布日期: 2026-10-06

备注: 26 pages, 8 figures. Accepted for publication in ACM Journal on Computing and Cultural Heritage

DOI: 10.1145/3836772

🔗 代码/项目: PROJECT_PAGE

💡 一句话要点

提出几何约束双向点云配准方法以解决薄型文物重建问题

🎯 匹配领域: 支柱四：生成式动作 (Generative Motion)

关键词: 点云配准 几何约束 文化遗产 非接触重建 厚度感知 旋转假设 三维重建

📋 核心要点

  1. 薄型文物的三维重建面临配准困难，现有方法在处理脆弱结构时常出现旋转模糊和不稳定性。
  2. 提出几何约束双向点云配准方法，结合语义引导预处理和厚度感知策略，以提高配准精度和稳定性。
  3. 实验结果显示，该方法在投影面积一致性和前后对齐方面优于现有技术，降低了错误翻转的风险。

📝 摘要（中文）

薄型文物的非接触三维重建面临显著的几何和配准挑战。由于其脆弱性，这些文物无法悬挂或使用人工标记，导致前后表面的独立获取。由于共享几何特征有限和缺乏明确的物理约束，后续配准困难，可能导致旋转模糊、不稳定和结构崩溃。为此，本文提出了一种专门针对薄型文物的几何约束双向点云配准方法，集成了语义引导预处理、基于主成分分析的几何归一化和厚度感知配准策略。通过评估有限的全局旋转假设，结合点到平面的迭代最近点算法，优化变换选择，实验结果表明该方法在大多数情况下表现出竞争力或改进，特别是在投影面积一致性和物理合理的前后对齐方面。

🔬 方法详解

问题定义：本文旨在解决薄型文物的非接触三维重建中，由于脆弱性导致的配准困难，现有方法在处理此类文物时常出现旋转模糊和结构不稳定的问题。

核心思路：提出的几何约束双向点云配准方法通过引入厚度感知和旋转假设评估，旨在提高配准的精度和稳定性，确保文物结构的完整性。

技术框架：该方法包括三个主要模块：语义引导预处理、基于主成分分析的几何归一化和厚度感知配准策略。首先进行语义预处理，然后进行几何归一化，最后通过厚度感知策略进行配准。

关键创新：最重要的创新点在于引入厚度作为几何约束，解决了旋转模糊问题，并通过评估有限的全局旋转假设来优化配准结果，与现有方法相比，显著提高了配准的可靠性。

关键设计：在技术细节上，采用点到平面的迭代最近点算法进行旋转假设的细化，优化变换选择时使用几何感知的适应度标准，确保配准结果的物理合理性。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，所提出的方法在投影面积一致性和前后对齐方面表现优异，尤其在处理薄型文物时，减少了错误翻转的风险。与基线方法相比，性能提升显著，确保了更高的配准精度和稳定性。

🎯 应用场景

该研究具有广泛的应用潜力，尤其在文化遗产保护和数字化领域。通过提供可靠的非接触三维重建方法，可以有效保护脆弱文物的结构完整性，促进文物的数字化保存和展示，推动文化遗产的研究与传播。

📄 摘要（原文）

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/.