Rotation Estimation Based on Serial Network and Application in Cave Buddha Statues

Abstract

Rotation estimation is one of the crucial elements of pose estimation. Accurate pose estimation facilitates the observation of objects in the metaverse and is also the most fundamental part of automated archaeological mapping. Many studies have been conducted on 6D pose estimation, but no systematic study has only been conducted on 3D rotation estimation. We systematically study the rotation estimation problem for 3D point cloud models. Firstly, we discuss the representation of rotation and how it affects the training of the neural network. After that, the dataset is created. A new method of generating labels is also proposed, in which two points are used as labels, with the two points being able to recover the initial orientation. Finally, a new serial network is proposed, which can be used to improve the performance of rotational estimation systems. The research described in this paper has been successfully applied to the restoration of the Huangze Temple Grotto Buddha statue in Guangyuan, Sichuan Province, providing a reliable scheme for solving the rotation estimation problem with faster convergence and higher accuracy.