Haofei Kuang

PhD Student
Contact:
Email: haofei.kuang@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 27 13
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.001
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Profiles: Google Scholar | Github

Short CV

Haofei Kuang is a PhD student at the University of Bonn since August 2021. He received his Master degree in Computer Science in 2021 and his Bachelor degree in Computer Science and Technology in 2017.

Research Interests

  • Mapping and Localization, Self-supervised Learning

Projects

  • HARMONY – Enhancing Healthcare with Assistive Robotic Mobile Manipulation

Publications

2023

  • H. Kuang, X. Chen, T. Guadagnino, N. Zimmerman, J. Behley, and C. Stachniss, “IR-MCL: Implicit Representation-Based Online Global Localization,” Ieee robotics and automation letters (ra-l), vol. 8, iss. 3, p. 1627–1634, 2023. doi:10.1109/LRA.2023.3239318
    [BibTeX] [PDF] [Code] 
    @article{kuang2023ral,
author = {Kuang, Haofei and Chen, Xieyuanli and Guadagnino, Tiziano and Zimmerman, Nicky and Behley, Jens and Stachniss, Cyrill},
title = {{IR-MCL: Implicit Representation-Based Online Global Localization}},
journal = ral,
volume = {8},
number = {3},
pages = {1627--1634},
doi = {10.1109/LRA.2023.3239318},
year = {2023},
codeurl = {https://github.com/PRBonn/ir-mcl},
}

2021

  • H. Kuang, Y. Zhu, Z. Zhang, X. Li, J. Tighe, S. Schwertfeger, C. Stachniss, and M. Li, “Video Contrastive Learning With Global Context,” in Proc. of the intl. conf. on computer vision workshops, 2021, pp. 3195-3204.
    [BibTeX] [PDF] [Code] 
    @inproceedings{kuang2021iccvws,
author = {Kuang, Haofei and Zhu, Yi and Zhang, Zhi and Li, Xinyu and Tighe, Joseph and Schwertfeger, S\"oren and Stachniss, Cyrill and Li, Mu},
title = {{Video Contrastive Learning With Global Context}},
booktitle = iccvws,
year = {2021},
pages = {3195-3204},
codeurl = {https://github.com/amazon-research/video-contrastive-learning},
url = {https://openaccess.thecvf.com/content/ICCV2021W/CVEU/papers/Kuang_Video_Contrastive_Learning_With_Global_Context_ICCVW_2021_paper.pdf},
}