Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving-Reference-Cited by-同舟云学术

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Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Dong Yinpeng¹,Kang Caixin²,Zhang Jinlai³,Zhu Zijian⁴,Wang Yikai¹,Yang Xiao¹,Su Hang¹,Wei Xingxing²,Zhu Jun¹

Affiliation:

1. Institute for AI, Tsinghua-Bosch Joint ML Center, Tsinghua-China Mobile Communications Group Co., Ltd. Joint Institute, Tsinghua University,Dept. of Comp. Sci. and Tech.

2. Institute of Artificial Intelligence, Beihang University

3. Guangxi University

4. Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University

Funder

National Key Research and Development Program of China

NSFC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10205484.pdf?arnumber=10205484

Reference70 articles.

1. Focal Sparse Convolutional Networks for 3D Object Detection

2. Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

3. Futr3d: A unified sensor fusion framework for 3d detection;chen;ArXiv Preprint,2022

4. FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection

5. Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

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4. Diffusion Models-Based Purification for Common Corruptions on Robust 3D Object Detection;Sensors;2024-08-22

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