An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation-Reference-Cited by-同舟云学术

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An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation

Published:2024-07 Issue:7 Volume:25 Page:8237-8247
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Wu Aotian¹,He Pan²^ORCID,Li Xiao¹,Chen Ke¹,Ranka Sanjay¹^ORCID,Rangarajan Anand¹^ORCID

Affiliation:

1. Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA

2. Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA

Funder

NSF

Florida Department of Transportation and FDOT District 5

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/6979/10582787/10537073-aam.pdf

Reference38 articles.

1. A Survey of Autonomous Driving: Common Practices and Emerging Technologies

2. Scalability in Perception for Autonomous Driving: Waymo Open Dataset

3. nuScenes: A Multimodal Dataset for Autonomous Driving

4. Vision meets robotics: The KITTI dataset

5. LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Data-driven Approach for Probabilistic Traffic Prediction and Simulation at Signalized Intersections;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

2. Point Cloud Automatic Annotation Framework for Autonomous Driving;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

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