TopoGAT: Plug-and-Play Topological Graph Attention for Fine-Grained 3D Segmentation

Fine-grained 3D point cloud segmentation is essential forrobot manipulation and CAD editing. A key unresolved challenge is thatdifferent parts of the same object often share similar local geometry, mak-ing part boundaries difficult to distinguish. Existing methods constructlocal patches via K-nearest search and rely on deeper and larger networksto learn discriminative features. However, these approaches mainly focuson local geometry and lack global structural awareness, which leads toambiguous predictions under sampling noise and partial observations.In this work, we propose Topological Graph Attention Network (To-poGAT), a lightweight, plug-and-play backbone refinement module thatintegrates topological data analysis with Graph Attention Networks tointroduce global structural information into point-wise feature learning.When combined with existing backbones, TopoGAT improves segmenta-tion accuracy with a slight parameter increase. Extensive experiments onfine-grained 3D part segmentation validate the effectiveness of the pro-posed TopoGAT and show up to 1.0% improvement on ShapeNetPartdataset and 3.3% improvement on S3DIS dataset.