Abstract
Deep learning (DL) models for 3D object detection from point clouds have shown remarkable progress in various autonomous perception scenarios. However, the issue of catastrophic forgetting seriously hinders the deployment of these models in real-world applications where new classes are encountered over time. In order to address this issue, we present the Contrastive Co-Teaching Network (COCO-TEACH) framework for class-incremental 3D object detection. Our proposed framework consists of two teacher networks: a primary teacher network that detects old class objects in new data and provides them with pseudo-labels and an auxiliary teacher network that leverages the unlabelled objects in new data. The two teacher models transfer their learned knowledge to the target student model through a class-aware consistency loss. To enhance this transfer, a supervised contrastive loss is further incorporated into the loss function. We evaluate the performance of our proposed method against baseline methods through extensive experiments on two benchmark datasets. The results show that our proposed framework achieves state-of-the-art performance on incremental 3D object detection.