High-Precision Registration of Grotto Point Clouds Using Multi-Source Data Fusion

Abstract. To address the challenges of large initial pose discrepancies in grotto point clouds acquired from multiple sources, complex local geometric structures, significant noise interference, and the tendency of traditional ICP algorithms to fall into local optima, a high-precision point cloud registration method is proposed by integrating feature extraction with the collaborative optimization of coarse and fine registration. This method first performs point cloud preprocessing through voxel downsampling and outlier removal; it then extracts stable feature regions based on normal vector estimation and curvature analysis, and constructs feature representations using FPFH descriptors; building on this, the K-4PCS algorithm is employed to perform coarse registration and obtain optimal initial transformation parameters, followed by fine registration using an improved ICP algorithm combined with KD-tree-based search optimization. The proposed method was validated using the STANFORD DRAGON dataset and the point cloud of the Buddha head statue from Cave 18 of the Yungang Grottoes. The results indicate that the proposed method effectively improves the convergence speed and accuracy of point cloud registration. It demonstrates good stability and applicability in complex cave heritage scenarios and can provide methodological support for the fusion of multi-source point clouds in the digital preservation of cultural heritage.