3D object detection and knowledge distillation in autonomous driving: A survey

Yan, Weiqing, Bu, Changhong, Zhang, Yuanyang, Yue, Guanghui, Zhou, Wei, Zhu, Xiatian and Han, Jungong 2027. 3D object detection and knowledge distillation in autonomous driving: A survey. Information Fusion: An International Journal on Multi-Sensor, Multi-Source Information Fusion 138 , 104706. 10.1016/j.inffus.2026.104706

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Abstract

Recent advances in autonomous driving have greatly promoted the development of 3D object detection. Nevertheless, state-of-the-art 3D detectors usually require large-scale networks and heavy computational costs, which severely restrict their deployment on resource-limited onboard platforms. Knowledge distillation (KD), which transfers knowledge from a high-capacity teacher model to a compact student model, has become an effective solution for improving the efficiency of 3D detection while maintaining high accuracy. This paper presents a comprehensive review of knowledge distillation methods for 3D object detection in autonomous driving. We first summarize the mainstream paradigms of 3D object detection, including LiDAR-based, camera-based, and multi-modal fusion frameworks, together with the fundamental categories of knowledge distillation. Then, existing KD-based 3D detection methods are systematically reviewed from three representative perspectives: LiDAR-to-LiDAR, LiDAR-to-camera, and multi-modal-to-single-modal distillation. The characteristics, advantages, and limitations of different distillation strategies are analyzed and compared in detail. Finally, we discuss the major challenges and open issues in current KD-based 3D object detection, and outline several promising directions for future research. This survey aims to provide a concise yet comprehensive reference for the design and deployment of efficient 3D perception systems in autonomous driving. We also provide a continuously updated project homepage to support ongoing research in this area here.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computational & Mathematical Sciences
Publisher:	Elsevier
ISSN:	1566-2535
Last Modified:	24 Aug 2026 11:33
URI:	https://orca.cardiff.ac.uk/id/eprint/189148

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