3D Generalized Category Discovery

Generalized category discovery (GCD) aims to recognize known categories and discover novel ones in unlabeled data, using supervision from known classes. 3D generalized category discovery (3DGCD) extends this setting to point clouds. Our analysis of pretrained 3D features reveals two problems. Feature neighborhoods contain objects from different categories and change across augmented views. Even when neighborhoods are relatively pure, known objects can dominate clustering and leave novel categories without distinct groups. We propose Geo3D to address both problems by coupling geometric relations between objects with category-level guidance. Geometric compatibility and agreement across views help identify reliable neighbors. Geo3D combines their relational evidence with a memory of category structure to construct discovery targets. Objects confidently assigned to known categories are anchored to their predicted classes, reducing their competition for novel groups during training. The resulting supervision supports coherent grouping while allowing novel categories to form distinct clusters. Extensive experiments on synthetic shapes and real-world scans demonstrate the effectiveness of the proposed method.