TOTMSeg: A Texture-Aware Octree-Based Transformer-Mamba Framework for Large-Scale Urban Mesh Semantic Segmentation

Semantic segmentation of 3D urban meshes is a fundamental task for intelligent urban scene understanding. However, conventional methods face severe computational efficiency bottlenecks in large-scale scenarios. Meanwhile, state space models (SSMs) with inherent linear time complexity offer an efficient solution, but existing SSM-based approaches fail to fully exploit the intrinsic textural properties of 3D meshes, resulting in degraded feature discriminability and inferior performance on fine-grained structures and small urban objects. To address these issues, we propose TOTMSeg, a texture-aware octree-based Transformer-Mamba framework for large-scale urban mesh semantic segmentation. It follows a coherent pipeline of texture feature extraction, octree-guided local feature aggregation, and global semantic feature modeling. Specifically, we design a Point-template-based Texture Sampling (PTS) strategy and integrate a lightweight Texture Triangular Convolution (TTC) module to extract fine-grained texture details, effectively alleviating the texture information deficiency in conventional point cloud-based methods. Extensive experiments on two large-scale public datasets demonstrate that TOTMSeg outperforms state-of-the-art (SOTA) baselines on main evaluation metrics, achieves notable performance gains on fine-grained structures and small-object categories, and preserves the high computational efficiency of SSM-based architectures.