LGSSM: Local-to-global State Space Model for Serialized Point Cloud Semantic Segmentation

Abstract. Point clouds have become essential data for describing real-world objects. Accurate and efficient 3D semantic segmentation plays a crucial role in environment understanding and scene reconstruction. However, current segmentation methods still face challenges from unordered data, high computational complexity, limited scene perception, and insufficient generalization. To address these issues, we propose a local-to-global semantic segmentation method based on a state-space model (LGSSM). Specifically, the proposed method uses three-dimensional serialization encoding to serialize point clouds along the x, y, and z directions, effectively addressing the inherent disorder of point clouds and enhancing spatial representation. Then, the local state space model extracts fine-grained local geometric structural information and the global state space model captures the overall scene representation, improving the modeling ability for both short and long distances. Finally, the serialized context aggregation module is utilized to fuse contextual features to promote spatial semantic consistency. Extensive experiments conducted on ScanNet, ScanNet200, and S3DIS demonstrate that our model achieves state-of-the-art segmentation accuracy compared with other existing methods.