MLGTM: Multi-Scale Local Geometric Transformer-Mamba Application in Terracotta Warriors Point Cloud Classification

Abstract

As an important representative of ancient Chinese cultural heritage, the classification of Terracotta Warriors point cloud data aids in cultural heritage preservation and digital reconstruction. However, these data face challenges such as complex morphological and structural variations, sparsity, and irregularity. This paper proposes a method named Multi-scale Local Geometric Transformer-Mamba (MLGTM) to improve the accuracy and robustness of Terracotta Warriors point cloud classification tasks. To effectively capture the geometric information of point clouds, we introduce local geometric encoding, including local coordinates and feature information, effectively capturing the complex local morphology and structural variations of the Terracotta Warriors and extracting representative local features. Additionally, we propose a multi-scale Transformer-Mamba information aggregation module, which employs a dual-branch Transformer with a Mamba structure and finally aggregates them on multiple scales to effectively handle the sparsity and irregularity of the Terracotta Warriors point cloud data. We conducted experiments on several datasets, including the ModelNet40, ScanObjectNN, ShapeNetPart, ETH, and 3D Terracotta Warriors fragment datasets. The results show that our method significantly improves the classification task of Terracotta Warriors point clouds, demonstrating strong accuracy.