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<p>This study proposes a 3D point cloud registration method that achieves high registration performance without the need for geometric feature extraction or correspondence matching. The proposed method first performs an initial alignment by estimating the principal axes between the source and the target using principal component analysis (PCA). Additionally, a plane constraint is incorporated to mitigate PCA instability by restricting rotation to a single axis when a dominant planar patch is detected. It then refines the alignment through a multi-stage Bayesian optimization process that separately optimizes rotation and translation in the 6 degrees of freedom (6-DOF) transformation space while progressively shrinking the search space. This method maintains stable registration performance without relying on feature matching and effectively achieves a balance between low computation time and high accuracy compared to conventional 6-DOF global optimization approaches. Experimental results on both custom single-object datasets and the KITTI Odometry dataset show that, although some limitations exist for geometrically symmetric objects, the proposed method generally demonstrates superior accuracy and real-time performance. This confirms the potential of Bayesian optimization as an effective and extensible method for real-time point cloud alignment.</p>

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Point Cloud Registration Using PCA and Hyperparameter Optimization

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Kisun Lee,
  • Youngjun Yoo,
  • Duhwan Mun

摘要

This study proposes a 3D point cloud registration method that achieves high registration performance without the need for geometric feature extraction or correspondence matching. The proposed method first performs an initial alignment by estimating the principal axes between the source and the target using principal component analysis (PCA). Additionally, a plane constraint is incorporated to mitigate PCA instability by restricting rotation to a single axis when a dominant planar patch is detected. It then refines the alignment through a multi-stage Bayesian optimization process that separately optimizes rotation and translation in the 6 degrees of freedom (6-DOF) transformation space while progressively shrinking the search space. This method maintains stable registration performance without relying on feature matching and effectively achieves a balance between low computation time and high accuracy compared to conventional 6-DOF global optimization approaches. Experimental results on both custom single-object datasets and the KITTI Odometry dataset show that, although some limitations exist for geometrically symmetric objects, the proposed method generally demonstrates superior accuracy and real-time performance. This confirms the potential of Bayesian optimization as an effective and extensible method for real-time point cloud alignment.