CLEAN: Category Knowledge-Driven Compression Framework for Efficient 3D Object Detection

作者：Haonan Zhang, Longjun Liu, Fei Hui, Bo Zhang, Hengmin Zhang, Zhiyuan Zha · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：2025 · DOI：10.1109/tpami.2025.3582706 · 被引用次数：3 · 研究领域：Advanced Neural Network Applications、Advanced Image and Video Retrieval Techniques、Medical Image Segmentation Techniques

Deep neural networks (DNNs) are potent in LiDAR-based 3D object detection (LiDAR-3DOD), yet their deployment remains daunting due to their cumbersome parameters and computations. Knowledge distillation (KD) is promising for compressing DNNs in LiDAR-3DOD. However, most existing KD methods transfer inadequate knowledge between homogeneous detectors, and do not thoroughly explore optimal student architectures, resulting in insufficient gains for compact student detectors. To this end, we propose a category knowledge-driven compression framework to achieve efficient LiDAR-based 3D detectors. Firstly, we distill knowledge from two-stage teacher detectors to one-stage student detectors, overcoming the limitations of homogeneous pairs. To conduct KD in these heterogeneous pairs, we explore the gap between heterogeneous detectors, and introduce category knowledge-driven KD (CaKD), which includes both student-oriented distillation and two-stage-oriented label assignment distillation. Secondly, to search for the optimal architecture of compact student detectors, we introduce a masked category knowledge-driven structured pruning scheme. This scheme evaluates filter importance by analyzing the changes in category predictions related to foreground regions before and after filter removal, and prunes the less important filters accordingly. Finally, we propose a modified IoU-aware redundancy elimination module to remove redundant false positive samples, thereby further improving the accurac...