CGRD: An Exemplar-Free Extension of Knowledge Distillation for Class-Incremental 3D Point Cloud Semantic Segmentation

Abstract

Class-incremental three-dimensional point cloud semantic segmentation requires models to learn newly introduced categories while preserving previously acquired knowledge without storing historical point clouds. This setting is challenged by representation drift during incremental optimization and semantic background shift caused by incomplete annotations of previously learned categories. To address these problems, this study proposes confidence-gated relational distillation, an exemplar-free teacher–student framework that combines feature-level relation preservation with semantic-level background correction. The relational component transfers normalized neighborhood-affinity distributions and weights each point according to teacher reliability, thereby reducing the influence of uncertain predictions. The background-compensation component reconstructs reliable old-class targets using class-specific thresholds and calibrates competition between previously learned and newly introduced classes. Experiments on the Stanford Large-Scale Three-Dimensional Indoor Spaces dataset and ScanNet show competitive performance across multiple incremental settings, with more consistent improvements on ScanNet. Under the ScanNet 10-1 protocol, the proposed method achieves an average mean intersection over union of 44.6% across eleven learning states and 33.3% at the final state. Under the same PointNet++ configuration, it also reduces training time and peak graphics processing unit memory. These results indicate that reliable relational transfer and adaptive background correction provide an effective balance between old-class retention and novel-class acquisition without introducing replay data or separate architectural branches during incremental optimization.