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<p>Point cloud registration plays a crucial role in preservation and digitization of cultural heritage by accurately aligning multiple point cloud datasets to create a complete 3D model. However, the complexity and diversity of objects lead to low-overlap and significant variations, posing challenges in achieving high accuracy, robustness and generalizability. This study proposes a Cross-Domain Multi-Channel Transformer (CDMCT) to address these challenges. The multi-channel dynamic encoding to enhance model’s sensitivity to local structures and angular relationships. The cross-domain convergence network preserves the global relationships within point cloud and the overall structural information. The integration of graph networks allows for flexible handling of local feature variations. We trained on 3DMatch and KITTI, and validated on cultural heritage datasets Terracotta Warriors and WHU-TLS ancient buildings. Experimental results show that CDMCT achieves significant improvements in registration accuracy and robustness, demonstrating its broad application potential in the digital preservation of cultural heritage.</p>

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Cross-Domain multi-channel transformer for point cloud registration in cultural heritage digital preservation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月9日

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  • Pengbo Zhou,
  • Li An,
  • Yong Wang,
  • Guohua Geng,
  • Yang Xu

摘要

Point cloud registration plays a crucial role in preservation and digitization of cultural heritage by accurately aligning multiple point cloud datasets to create a complete 3D model. However, the complexity and diversity of objects lead to low-overlap and significant variations, posing challenges in achieving high accuracy, robustness and generalizability. This study proposes a Cross-Domain Multi-Channel Transformer (CDMCT) to address these challenges. The multi-channel dynamic encoding to enhance model’s sensitivity to local structures and angular relationships. The cross-domain convergence network preserves the global relationships within point cloud and the overall structural information. The integration of graph networks allows for flexible handling of local feature variations. We trained on 3DMatch and KITTI, and validated on cultural heritage datasets Terracotta Warriors and WHU-TLS ancient buildings. Experimental results show that CDMCT achieves significant improvements in registration accuracy and robustness, demonstrating its broad application potential in the digital preservation of cultural heritage.