Diffusion Transformer for point cloud registration: digital modeling of cultural heritage

Abstract Digital modeling is an essential means for preserving and passing down historical culture within cultural heritage. Point cloud registration technology, by aligning point cloud data captured from multiple perspectives, enhances the accuracy of reconstructing the complex structures of artifacts and buildings and provides a reliable digital foundation for their protection, exhibition, and research. Due to the challenges posed by complex morphology, noise, and missing data when processing cultural heritage data, this paper proposes a point cloud registration method based on the Diffusion Transformer (PointDT). Compared to traditional methods, the Diffusion Transformer can better capture both the global features and local structures of point cloud data, more accurately capturing the geometric and semantic information of the target point cloud, thereby achieving precise digital reconstruction. In this study, we trained our method using indoor datasets such as 3DMatch and large-scale outdoor datasets like KITTI, and validated it on various cultural heritage datasets, including those of the Terracotta Warriors and heritage buildings. The results demonstrate that this method not only significantly improves accuracy but also shows advantages in computational efficiency.