Efficient Distillation Using Channel Pruning for Point Cloud-Based 3D Object Detection
文献类型：期刊论文
| 作者 | Li, Fuyang3; Min, Chen2; Wang, Juan1; Xiao, Liang3; Zhao, Dawei3; Nie, Yiming3; Dai, Bin3 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2025-06-13 |
| 页码 | 15 |
| 关键词 | Knowledge distillation 3D object detection point cloud point cloud network pruning network pruning autonomous driving autonomous driving autonomous driving |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2025.3574213 |
| 英文摘要 | Although point cloud-based 3D object detectors have advanced significantly in recent years, they are frequently hindered by substantial computational overheads. Lightweight model techniques, such as knowledge distillation, have recently been proven effective for 3D object detector compression. However, neural network pruning's complementary role in knowledge distillation is often overlooked. In this paper, we propose an efficient distillation using channel pruning for point cloud-based 3D object detection. Firstly, given the complete teacher model, we introduce random and magnitude channel pruning methods to generate several compact student models and investigate the effects of different combinations on 3D and 2D layers. Secondly, we introduce model compression scores to explore the impact of channel compression ratios and input resolutions, enabling us to select suitable pruned models for distillation from the given set. Furthermore, we employ multi-source knowledge distillation to facilitate more effective spatial and semantic knowledge transfer. To highlight the features of the foreground regions during distillation, we then propose a soft pivotal position selection mask. Extensive evaluations on various datasets using both pillar-and voxel-based 3D detectors validate the efficiency of our method in compressing point cloud-based 3D detectors. Codes are publicly available at https://github.com/lifuyang-1919/Efficient-Distillation.git |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001508152800001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42355]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Xiao, Liang |
| 作者单位 | 1.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Chinese Acad Mil Sci, Def Innovat Inst, Beijing 100071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Fuyang,Min, Chen,Wang, Juan,et al. Efficient Distillation Using Channel Pruning for Point Cloud-Based 3D Object Detection[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2025:15. |
| APA | Li, Fuyang.,Min, Chen.,Wang, Juan.,Xiao, Liang.,Zhao, Dawei.,...&Dai, Bin.(2025).Efficient Distillation Using Channel Pruning for Point Cloud-Based 3D Object Detection.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,15. |
| MLA | Li, Fuyang,et al."Efficient Distillation Using Channel Pruning for Point Cloud-Based 3D Object Detection".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2025):15. |
入库方式： OAI收割
来源：计算技术研究所
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