Enhancing point cloud registration with transformer: cultural heritage protection of the Terracotta Warriors

Abstract Point cloud registration technology, by precisely aligning repair components with the original artifacts, can accurately record the geometric shape of cultural heritage objects and generate three-dimensional models, thereby providing reliable data support for the digital preservation, virtual exhibition, and restoration of cultural relics. However, traditional point cloud registration methods face challenges when dealing with cultural heritage data, including complex morphological and structural variations, sparsity and irregularity, and cross-dataset generalization. To address these challenges, this paper introduces an innovative method called Enhancing Point Cloud Registration with Transformer (EPCRT). Firstly, we utilize local geometric perception for positional encoding and combine it with a dynamic adjustment mechanism based on local density information and geometric angle encoding, enhancing the flexibility and adaptability of positional encoding to better characterize the complex local morphology and structural variations of artifacts. Additionally, we introduce a convolutional-Transformer hybrid module to facilitate interactive learning of artifact point cloud features, effectively achieving local–global feature fusion and enhancing detail capture capabilities, thus effectively handling the sparsity and irregularity of artifact point cloud data. We conduct extensive evaluations on the 3DMatch, ModelNet, KITTI, and MVP-RG datasets, and validate our method on the Terracotta Warriors cultural heritage dataset. The results demonstrate that our method has significant performance advantages in handling the complexity of morphological and structural variations, sparsity and irregularity of relic data, and cross-dataset generalization.