Class-incremental learning (CIL) thrives due to its success in processing the influx of information by learning from continuously added new classes while preventing catastrophic forgetting about the old ones. It is essential for the performance breakthrough of CIL to effectively refine past knowledge from the base model and balance it with new learning. However, such an issue has not yet been considered in current research. In this work, we explore the potential of CIL from these perspectives and propose a novel balanced residual distillation framework (BRD-CIL) to push the performance bar of CIL to a new higher level. Specifically, BRD-CIL designs a residual distillation learning strategy, which can dynamically expand the network structure to capture the residuals between the base and target models, effectively refining the past knowledge. Furthermore, BRD-CIL designs a balanced pseudo-label learning strategy by generating a guidance mask to reduce the preference for old classes, ensuring balanced learning from new and old classes. We apply the proposed BRD-CIL to a challenging 3D point cloud semantic segmentation task where the data are unordered and unstructured. Extensive experimental results demonstrate that BRD-CIL sets a new benchmark with an outstanding balance capability in class-biased scenarios.

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