GLA: Global-Local Awareness for 3D Point Cloud Class-Incremental Semantic Segmentation

Semantic segmentation of 3D point clouds has garnered considerable attention in academic research and industrial applications. However, existing methods typically assume fixed semantic classes, which is unrealistic for practical scenarios where new classes emerge incrementally. This leads to catastrophic forgetting of previous knowledge and semantic shifts where annotations treat previous classes as background in new tasks. Moreover, the unordered and unstructured properties of point clouds further exacerbate catastrophic forgetting. To address these, we propose the Global-Local Awareness for 3D point cloud class-incremental semantic segmentation (i.e., GLA). Specifically, we introduce a global-aware modeling network (GAMN) based on the state space model, providing robust feature representations. Furthermore, to alleviate catastrophic forgetting, we propose a Graph Attention Knowledge Distillation (GAKD) module. GAKD adaptively integrates local geometric features through an attention mechanism, emphasizing the distillation of geometric structural knowledge. Notably, our approach integrates global context-awareness and local attention through the state space model and GAKD module, respectively. In addition, we introduce pseudo-labeling to alleviate semantic shift. Experiments on the S3DIS dataset validate the superiority of our approach.