Focal Distillation From High-Resolution Data to Low-Resolution Data for 3D Object Detection-Reference-Cited by-同舟云学术

Focal Distillation From High-Resolution Data to Low-Resolution Data for 3D Object Detection

Published:2023-12 Issue:12 Volume:24 Page:14064-14075
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Shan Jiawei¹^ORCID,Zhang Gang²,Tang Chufeng²,Pan Hujie³,Yu Qiankun³,Wu Guanhao¹^ORCID,Hu Xiaolin²^ORCID

Affiliation:

1. Department of Precision Instrument, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China

2. Department of Computer Science and Technology, Institute for Artificial Intelligence, and the State Key Laboratory of Intelligent Technology and Systems, BNRist, Tsinghua University, Beijing, China

3. SAIC Intelligent Technology, Shanghai, China

Funder

Beijing Science and Technology Planning Project

National Natural Science Foundation of China

Shanghai Automotive Industry Corporation (SAIC) Intelligent Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10339106/10230995.pdf?arnumber=10230995

Reference58 articles.

1. Voxel R-CNN: Towards high performance voxel-based 3D object detection;deng;arXiv 2012 15712,2020

2. End-to-end multi-view fusion for 3D object detection in LiDAR point clouds;zhou;Proc Conf Robot Learn,2020

3. Private Model Compression via Knowledge Distillation

4. PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection

5. Multi-view 3D Object Detection Network for Autonomous Driving

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