MambaMeshSeg-Net: A Large-Scale Urban Mesh Semantic Segmentation Method Using a State Space Model with a Hybrid Scanning Strategy

Abstract

Semantic segmentation of urban meshes plays an increasingly crucial role in the analysis and understanding of 3D environments. Most existing large-scale urban mesh semantic segmentation methods focus on integrating multi-scale local features but struggle to model long-range dependencies across facets effectively. Furthermore, owing to high computational complexity or excessive pre-processing operations, these methods lack the capability for the efficient semantic segmentation of large-scale urban meshes. Inspired by Mamba, we propose MambaMeshSeg-Net, a novel 3D urban mesh semantic segmentation method based on the State Space Model (SSM). The proposed method incorporates a hybrid scanning strategy that adaptively scans 3D urban meshes to extract long-range dependencies across facets, enhancing semantic segmentation performance. Moreover, our model exhibits faster performance in both inference and pre-processing compared to other mainstream models. In comparison with existing state-of-the-art (SOTA) methods, our model demonstrates superior performance on two widely utilized open urban mesh datasets.

Keywords

• 3D urban meshes
• State Space Model
• semantic segmentation
• hybrid scanning strategy
• spatial features