In our research, we introduce a novel approach to knowledge distillation aimed at enhancing the computational efficiency of 3D object detection within a teacher-student framework. The essence of our method lies in enabling the student model to distill knowledge from the teacher model, thereby reducing computational complexity while minimizing the performance gap between the two models throughout the training process. Traditionally, knowledge distillation techniques have primarily focused on improving the performance of classifiers and have often proven inapplicable or less effective for 3D object detection tasks. _x000D_
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<br>To address this problem, we proposed a method using an autoencoder to effectively distill the teacher’s fused information into the student’s BEV through knowledge distillation. This enables the student model to learn important but difficult-to-capture feature representations from the teacher model, thus allowing it to learn effectively and efficiently. Moreover, we introduce a training strategy that not only reduces the parameters of the student network but also enhances its performance compared to existing models. This dual objective of parameter reduction and performance improvement is achieved through careful design choices and optimization techniques, ensuring that the student model achieves competitive results with fewer computational resources. To validate the efficacy of our proposed methodology, we conduct comprehensive experiments using the nuScenes dataset, a widely used benchmark in the field of 3D object detection. Our experiments are based on the ResNet[16] model architecture, which serves as the backbone for both the teacher and student networks. Through rigorous experimentation and evaluation, we demonstrate the effectiveness and practical applicability of our approach in the context of real-world object detection tasks.
