The virtual reassembly of fragmented cultural relics remains a challenging task due to incomplete contours, complex fracture geometries, and the lack of reliable accuracy evaluation when ground-truth models are unavailable. To address these issues, this study proposes an automated virtual reassembly framework based on multi-feature extraction and hierarchical fragment matching. First, contour points are extracted from fragment point clouds using neighborhood roughness analysis and further refined through a Cylinder Box-based completion strategy to recover missing contour segments. Then, multiple complementary features, including Fast Point Feature Histograms (FPFHs), Heat Kernel Signatures (HKSs), and a spatial cube-based contour shape descriptor, are jointly constructed to characterize both local geometric details and global structural properties of fragments. To improve matching efficiency and robustness, a tree-based fragment retrieval strategy combined with a coarse-to-fine registration scheme is employed to identify adjacent fragments while reducing computational complexity. In addition, a pseudo-ground-truth accuracy evaluation method is introduced to quantitatively assess cumulative reassembly errors in the absence of reliable reference data. Experiments conducted on the public Buddha head dataset demonstrate that the proposed method achieves stable and visually consistent reassembly results, with a cumulative error as low as 1.58%, while significantly reducing retrieval computations compared with exhaustive matching strategies. These results indicate that the proposed framework provides a practical and verifiable solution for the automated digital restoration of fragmented cultural relics.
Zhao et al. (Sun,) studied this question.