Breaking the Model Forgetting Cycle in Long-Incremental 3D Object Detection

Incremental 3D object detection requires a detector to learnnovel object classes while remembering previously learned ones oversequentially arriving data. Previous methods, primarily based on pseudo-labeling, perform reasonably in short-incremental stages but still suf-fer from severe model forgetting when dealing with long-incrementalsequences. We investigate this failure and reveal a detrimental self-reinforcing cycle: data distribution shift of novel classes causes modelforgetting on old classes, which further produces accumulated error inpseudo-labeling that exacerbates model degradation. To address thisissue, we draw inspiration from the human learning process and proposethe Learning-Dynamics-driven Memory and Review (LDMR) framework.LDMR monitors per-class detection quality at periodic training check-points and uses these learning-dynamics signals to drive two innova-tive mechanisms, namely (i) human-like intra-stage review that divideseach incremental stage into multiple sub-stages’ training and concen-trates on remembering the most-forgotten objects, and (ii) scene-awarecross-stage memory evolution that evolves a memory bank to transferknowledge between two consecutive stages by jointly considering scenelearnability and diversity. Extensive experiments across multiple long-incremental protocols on indoor benchmarks SUN RGB-D and Scan-NetV2 show that LDMR substantially mitigates the model forgettingand outperforms all baselines by a clear margin. Code is available athttps://github.com/qianpeisheng/LDMR.