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Other IDs

Scopus Author ID: 57756416100
Scopus Author ID: 57756416100

Keywords

Computer Vision, Visual Recognition, Machine Learning

Countries

United Kingdom, China

Biography

I am a postdoctoral researcher in the King’s Laboratory for Intelligent Computing (KLIC) at King’s College London, working with Prof Bipin Rajendran. My research interest is on analog in-memory-computing (AIMC) and hardware acceleration algorithms for artificial intelligence.

Before that, I completed my PhD in Computer Science at Durham University, supervised by Prof Toby Breckon and Prof Hubert P. H. Shum. My research pursues to better 3D perception and semantic scene understanding based on computer vision and deep learning. I was awarded as the runner-up for the 2024 BMVA Sullivan Doctoral Thesis Prize in recognition of my contributions to computer vision research in the UK.

Prior to Durham, I received my bachelor’s degree in Telecommunications Engineering at Nanjing University of Science and Technology (NJUST) in 2020. In NJUST, I worked on semantic scene understanding for UAVs and UGVs with Prof Hong Gu.

Activities

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Employment (2)

King's College London: London, GB

Employment
Source: check_circle
King's College London

King's College London: London, GB

2024-08 to present | Postdoctoral Researcher (King's Laboratory for Intelligent Computing)
Employment
Source: Self-asserted source
Li Li

Education and qualifications (2)

Durham University: Durham, GB

2020-10-01 to 2024-07 | PhD (Department of Computer Science)
Education
Source: Self-asserted source
Li Li

Nanjing University of Science and Technology: Nanjing, Jiangsu, CN

2016-09 to 2020-06-19 | Bachelor of Engineering (School of Electronic and Optical Engineering)
Education
Source: Self-asserted source
Li Li

Professional activities (3)

The British Machine Vision Association (BMVA): Durham, GB

2024 | The Sullivan Doctoral Thesis Prize - runner-up
Distinction
Source: Self-asserted source
Li Li

Nanjing University of Science and Technology: Nanjing, Jiangsu, CN

2020-06-19 | Outstanding Graduate (School of Electronic and Optical Engineering)
Distinction
Source: Self-asserted source
Li Li

Science and Technology Association, Nanjing Univeristy of Sci. & Tech.: Nanjing, Jiangsu, CN

2018-06 to 2019-06 | Chairman (School of Electronic and Optical Engineering)
Membership
Source: Self-asserted source
Li Li

Funding (1)

Jiangsu Government Scholarship for Overseas Study

2017-07 to 2017-08 | Award
Jiangsu Provincial Department of Education (Nanjing, Jiangsu, CN)
GRANT_NUMBER: 1
Source: Self-asserted source
Li Li

Works (10)

RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2025 | Conference paper
DOI: 10.1007/978-3-031-72667-5_13
EID:

2-s2.0-85206135483

Part of ISBN: 9783031726668
Part of ISSN: 16113349 03029743
Contributors: Li, L.; Shum, H.P.H.; Breckon, T.P.
Source: Self-asserted source
Li Li via Scopus - Elsevier

An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models

arXiv
2024 | Other
DOI: 10.48550/arXiv.2405.14870
EID:

2-s2.0-85195524657

Part of ISSN: 23318422
Contributors: Sun, J.; Xu, X.; Kong, L.; Liu, Y.; Li, L.; Zhu, C.; Zhang, J.; Xiao, Z.; Chen, R.; Wang, T. et al.
Source: Self-asserted source
Li Li via Scopus - Elsevier

DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications

arXiv
2024 | Other
DOI: 10.48550/arXiv.2406.10068
EID:

2-s2.0-85197782634

Part of ISSN: 23318422
Contributors: Li, L.; Ismail, K.N.; Shum, H.P.H.; Breckon, T.P.
Source: Self-asserted source
Li Li via Scopus - Elsevier

RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation

arXiv
2024 | Other
DOI: 10.48550/arXiv.2407.10159
EID:

2-s2.0-85200612516

Part of ISSN: 23318422
Contributors: Li, L.; Shum, H.P.H.; Breckon, T.P.
Source: Self-asserted source
Li Li via Scopus - Elsevier
grade
Preferred source (of 2)‎

TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training

arXiv
2024 | Other
DOI: 10.48550/arXiv.2408.13902
EID:

2-s2.0-85204256916

Part of ISSN: 23318422
Contributors: Li, L.; Qiao, T.; Shum, H.P.H.; Breckon, T.P.
Source: Self-asserted source
Li Li via Scopus - Elsevier

Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation

arXiv
2023 | Other
DOI: 10.48550/arXiv.2303.11203
EID:

2-s2.0-85151746302

Part of ISSN: 23318422
Contributors: Li, L.; Shum, H.P.H.; Breckon, T.P.
Source: Self-asserted source
Li Li via Scopus - Elsevier

Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2023 | Conference paper
DOI: 10.1109/CVPR52729.2023.00903
EID:

2-s2.0-85173918257

Part of ISBN: 9798350301298
Part of ISSN: 10636919
Contributors: Li, L.; Shum, H.P.H.; Breckon, T.P.
Source: Self-asserted source
Li Li via Scopus - Elsevier

Less Is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation

Conference on Computer Vision and Pattern Recognition (CVPR)
2023-06 | Conference paper
ARXIV: https://arxiv.org/abs/2303.11203
Source: Self-asserted source
Li Li

DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications

Proceedings - 2021 International Conference on 3D Vision, 3DV 2021
2021 | Conference paper
DOI: 10.1109/3DV53792.2021.00130
EID:

2-s2.0-85125015308

Part of ISBN: 9781665426886
Contributors: Li, L.; Ismail, K.N.; Shum, H.P.H.; Breckon, T.P.
Source: Self-asserted source
Li Li via Scopus - Elsevier

DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications

International Conference on 3D Vision (3DV)
2021-10-15 | Conference paper
ARXIV: https://arxiv.org/abs/2406.10068
Source: Self-asserted source
Li Li

Peer review (1 review for 1 publication/grant)

Review activity for Pattern recognition. (1)
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