Boosting 3D Object Detection via Self-Distilling Introspective Data

3D object detection is a fundamental yet critical task for autonomous driving. In this paper, we investigate a novel self-distilling paradigm by proposing Self-distilling Introspective Data (SID) to boost the accuracy of 3D object detection in both LiDAR-based and LiDAR-Camera-based scenarios. The proposed SID significantly improves the applicability of the distillation approach since it does not require extra training data or complex teacher network design. Specifically, we first employ an introspective data augmentation method to enrich object-aware information in sparse point clouds through geometric or semantic injection. We then utilize this enhanced data to train a robust teacher model. In contrast to traditional distillation that relies on larger models to enhance the representations of smaller ones, the teacher model in SID shares the same architecture as the student model but exhibits exceptionally high discriminative ability. This enables the effective transfer of rich feature representations to the student model. Rooted on such a scheme, when conducting LiDAR-based detectors, SID significantly enhances the semantic representation capabilities of sparse point clouds. Additionally, in the LiDAR-Camera-based setting, SID also effectively supervises the fusion of the two modalities at the feature level, ensuring more reasonable cross-modal learning. Extensive experiments show the proposed SID improves a variety of detectors. For the LiDAR-based detector, the SID gains 2.31% mAP improvements for the hard objects in KITTI, while 1.76% NDS improvements on nuScenes. For the LiDAR-Camera-based detectors, the SID boosts the detection accuracy significantly, with 1.5% mAP promotion on KITTI and 2.15% NDS improvements on the nuScenes benchmark.