Gim Hee Lee

9.6k total citations · 3 hit papers
88 papers, 3.6k citations indexed

About

Gim Hee Lee is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Computational Mechanics. According to data from OpenAlex, Gim Hee Lee has authored 88 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 76 papers in Computer Vision and Pattern Recognition, 39 papers in Aerospace Engineering and 21 papers in Computational Mechanics. Recurrent topics in Gim Hee Lee's work include Advanced Vision and Imaging (40 papers), Robotics and Sensor-Based Localization (39 papers) and Advanced Image and Video Retrieval Techniques (21 papers). Gim Hee Lee is often cited by papers focused on Advanced Vision and Imaging (40 papers), Robotics and Sensor-Based Localization (39 papers) and Advanced Image and Video Retrieval Techniques (21 papers). Gim Hee Lee collaborates with scholars based in Singapore, Switzerland and China. Gim Hee Lee's co-authors include Marc Pollefeys, Zi Jian Yew, Friedrich Fraundorfer, Lionel Heng, Petri Tanskanen, Lorenz Meier, Sixing Hu, Xun Xu, Jiaxin Li and Na Zhao and has published in prestigious journals such as International Journal of Computer Vision, The International Journal of Robotics Research and IEEE Transactions on Circuits and Systems for Video Technology.

In The Last Decade

Gim Hee Lee

81 papers receiving 3.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

RPM-Net: Robust Point Matching Using Learned Features

2020 366 citations Gim Hee Lee et al. arXiv (Cornell University) profile →
PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

2012 306 citations Gim Hee Lee, Friedrich Fraundorfer et al. profile →
REGTR: End-to-end Point Cloud Correspondences with Transformers

2022 156 citations Gim Hee Lee et al. profile →

Peers

Gim Hee Lee

CVPR

AE

GEOLO

CM

EE

Andrew Johnson United States

Luigi Di Stefano Italy

Zoltán-Csaba Márton Germany

Christopher Zach United States

Shaoshuai Shi Hong Kong

Jaesik Park South Korea

Yuchao Dai China

Nico Blodow Germany

Jianwei Wan China

Richard A. Newcombe United Kingdom

Andrew Johnson United States

Gim Hee Lee

2.9k ×1.3

CVPR

3.3k ×1.7

AE

1.1k ×1.1

GEOLO

834 ×1.0

CM

497 ×0.9

EE

Citations per year, relative to Gim Hee Lee Gim Hee Lee (= 1×) peers Andrew Johnson

Countries citing papers authored by Gim Hee Lee

Since Specialization

Citations

This map shows the geographic impact of Gim Hee Lee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gim Hee Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gim Hee Lee more than expected).

Fields of papers citing papers by Gim Hee Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gim Hee Lee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gim Hee Lee. The network helps show where Gim Hee Lee may publish in the future.

Co-authorship network of co-authors of Gim Hee Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Gim Hee Lee. A scholar is included among the top collaborators of Gim Hee Lee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gim Hee Lee. Gim Hee Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Zhu, Yaohui, et al.. (2025). NavRAG: Generating User Demand Instructions for Embodied Navigation through Retrieval-Augmented LLM. 8430–8440.

2.

Lee, Gim Hee, et al.. (2025). TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree Sequencing. 26608–26617. 1 indexed citations

3.

Zhang, Yongqiang, et al.. (2024). Towards Non Co-occurrence Incremental Object Detection with Unlabeled In-the-Wild Data. International Journal of Computer Vision. 132(11). 5066–5083.

4.

Lee, Gim Hee, et al.. (2024). Robust Visual Recognition with Class-Imbalanced Open-World Noisy Data. Proceedings of the AAAI Conference on Artificial Intelligence. 38(15). 16989–16997. 1 indexed citations

5.

Li, Chen, et al.. (2024). DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRF. 20561–20570. 5 indexed citations

6.

Pan, Liang, et al.. (2024). Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption. 1011–1021. 2 indexed citations

7.

Nguyen, Khôi, Chen Li, & Gim Hee Lee. (2024). ESCAPE: Encoding Super-keypoints for Category-Agnostic Pose Estimation. 23491–23500.

8.

Yan, Zhiwen, et al.. (2024). Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering. 20923–20931. 28 indexed citations

9.

Zhang, Can, et al.. (2023). Unsupervised Feature Representation Learning for Domain-generalized Cross-domain Image Retrieval. 10982–10991. 6 indexed citations

10.

Zhao, Yuyang, et al.. (2023). Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization. International Journal of Computer Vision. 132(3). 837–853. 22 indexed citations

11.

Dong, Na, Yongqiang Zhang, Mingli Ding, & Gim Hee Lee. (2023). Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 37(1). 543–551. 24 indexed citations

12.

Li, Chen & Gim Hee Lee. (2021). From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation. 1482–1491. 56 indexed citations

13.

Dong, Na, Yongqiang Zhang, Mingli Ding, & Gim Hee Lee. (2021). Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection. arXiv (Cornell University). 4 indexed citations

14.

Pan, Liang, et al.. (2020). Robust 6D Object Pose Estimation by Learning RGB-D Features. 6218–6224. 26 indexed citations

15.

Xu, Xun & Gim Hee Lee. (2020). Weakly Supervised Semantic Point Cloud Segmentation: Towards 10× Fewer Labels. 13703–13712. 167 indexed citations

16.

Li, Jiaxin & Gim Hee Lee. (2019). USIP: Unsupervised Stable Interest Point Detection From 3D Point Clouds. 361–370. 128 indexed citations

17.

Hu, Sixing & Gim Hee Lee. (2019). Image-Based Geo-Localization Using Satellite Imagery. International Journal of Computer Vision. 128(5). 1205–1219. 49 indexed citations

18.

Pan, Liang, Chee–Meng Chew, & Gim Hee Lee. (2019). PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds.. arXiv (Cornell University). 5 indexed citations

19.

Saurer, Olivier, Marc Pollefeys, & Gim Hee Lee. (2016). Sparse to Dense 3D Reconstruction from Rolling Shutter Images. 3337–3345. 25 indexed citations

20.

Lee, Gim Hee, Friedrich Fraundorfer, & Marc Pollefeys. (2011). MAV visual SLAM with plane constraint. Zenodo (CERN European Organization for Nuclear Research). 15 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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