3DEKD: 3D Explanation-Based Knowledge Distillation for Pillar-Based 3D Object Detection

Heejung Choi, Dabin Kang, Dong-hun Lee, Jaehyup Lee, Sang-hyo Park

Abstract: LiDAR-based 3D object detection has been widely utilized in fields such as autonomous driving and robotics. However, the black-box nature of 3D models limits their interpretability, making it difficult to understand their predictions and evaluate significant feature contributions, which are essential for safety-critical applications. To address this, we propose a novel knowledge distillation method for 3D object detection that integrates explanation-based and aggregation techniques to achieve effective knowledge transfer and, as a result, enhance the model’s interpretability. Our method generates attribution maps that highlight the importance of 3D points in the teacher model and aggregates them into a single map. This map is aligned with the student model’s pillar features using corresponding coordinates, allowing pillar-wise feature mapping. Building on this feature mapping, to the best of our knowledge, this is the first study to propose a distillation process that effectively transfers the teacher model’s explanations of critical regions to the student model. Experimental results demonstrate that the proposed method increases 3D and BEV mAP by up to 2.09% and 0.84%, respectively, compared to the existing models.

External IDs:doi:10.1109/lsp.2025.3590327