Weight Pruning to Mitigate Class-Specific Accuracy Degradation for LiDAR-Based 3D Object Detection

The realization of autonomous driving systems requires efficient and accurate 3D object detection to identify objects such as vehicles, pedestrians, and cyclists within the driving environment using point cloud data. For achieving both high speed processing and high accuracy, it is necessary to reduce model size by model compression techniques, such as pruning, while maintaining performance. However, pruning for 3D object detection tasks has not been extensively studied, and the effects of applying existing pruning methods to 3D object detection models remain unclear. In this paper, we clarify the problems of pruning 3D object detection models with existing methods through preliminary experiments, and propose a pruning method suitable for 3D object detection models that solves these problems. Our preliminary experiments reveal that existing pruning methods significantly degrade detection performance for specific object classes. To address this issue, we propose a pruning method that preserves class-specific knowledge, mitigating biased accuracy degradation across different object classes. Experimental results on the KITTI dataset demonstrate that the proposed method can be combined with existing pruning methods without conflicts and achieves higher accuracy than existing methods.