Jingnan Shi

1.2k total citations · 1 hit paper
12 papers, 732 citations indexed

About

Jingnan Shi is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Control and Systems Engineering. According to data from OpenAlex, Jingnan Shi has authored 12 papers receiving a total of 732 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 7 papers in Aerospace Engineering and 4 papers in Control and Systems Engineering. Recurrent topics in Jingnan Shi's work include Robotics and Sensor-Based Localization (7 papers), Robot Manipulation and Learning (4 papers) and Image Processing and 3D Reconstruction (2 papers). Jingnan Shi is often cited by papers focused on Robotics and Sensor-Based Localization (7 papers), Robot Manipulation and Learning (4 papers) and Image Processing and 3D Reconstruction (2 papers). Jingnan Shi collaborates with scholars based in United States, China and South Korea. Jingnan Shi's co-authors include Luca Carlone, Heng Yang, Dominic Maggio, Courtney Mario, Yun Chang, A. Chatterjee, Antoni Rosinol, Benjamin Morrell, Kamak Ebadi and Muhammad Fadhil Ginting and has published in prestigious journals such as IEEE Transactions on Robotics, Gait & Posture and Clinical Biomechanics.

In The Last Decade

Jingnan Shi

10 papers receiving 712 citations

Hit Papers

TEASER: Fast and Certifiable Point Cloud Registration 2020 2026 2022 2024 2020 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jingnan Shi United States 6 512 353 260 163 139 12 732
Haoang Li Hong Kong 12 427 0.8× 404 1.1× 216 0.8× 97 0.6× 83 0.6× 36 605
Dmitry Stepanov Russia 4 512 1.0× 474 1.3× 272 1.0× 160 1.0× 159 1.1× 8 794
Narunas Vaškevičius Germany 15 714 1.4× 553 1.6× 353 1.4× 210 1.3× 57 0.4× 44 909
John McCormac United Kingdom 3 540 1.1× 613 1.7× 220 0.8× 82 0.5× 79 0.6× 3 777
Zeyu Hu China 6 409 0.8× 536 1.5× 200 0.8× 151 0.9× 159 1.1× 11 810
Masashi Yokozuka Japan 10 415 0.8× 257 0.7× 200 0.8× 174 1.1× 31 0.2× 38 555
Ignacio Vizzo Germany 12 775 1.5× 722 2.0× 457 1.8× 575 3.5× 325 2.3× 18 1.4k
Yiyi Liao China 15 307 0.6× 827 2.3× 152 0.6× 132 0.8× 270 1.9× 41 1.1k
Philip David United States 12 414 0.8× 656 1.9× 199 0.8× 207 1.3× 184 1.3× 33 986
Álvaro Parra Australia 9 285 0.6× 211 0.6× 150 0.6× 124 0.8× 68 0.5× 15 439

Countries citing papers authored by Jingnan Shi

Since Specialization
Citations

This map shows the geographic impact of Jingnan Shi'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 Jingnan Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jingnan Shi more than expected).

Fields of papers citing papers by Jingnan Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jingnan Shi. 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 Jingnan Shi. The network helps show where Jingnan Shi may publish in the future.

Co-authorship network of co-authors of Jingnan Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Jingnan Shi. A scholar is included among the top collaborators of Jingnan Shi 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 Jingnan Shi. Jingnan Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Shi, Jingnan, et al.. (2025). CRISP: Object Pose and Shape Estimation with Test-Time Adaptation. 11644–11653. 1 indexed citations
2.
4.
Lim, Hyungtae, Jingnan Shi, Ignacio Vizzo, et al.. (2025). KISS-Matcher: Fast and Robust Point Cloud Registration Revisited. 11104–11111. 2 indexed citations
5.
Shi, Jingnan, Rajat Talak, Dominic Maggio, & Luca Carlone. (2023). A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training. 2 indexed citations
6.
Maggio, Dominic, et al.. (2023). Loc-NeRF: Monte Carlo Localization using Neural Radiance Fields. 4018–4025. 55 indexed citations
7.
Shi, Jingnan, Heng Yang, & Luca Carlone. (2023). Optimal and Robust Category-Level Perception: Object Pose and Shape Estimation From 2-D and 3-D Semantic Keypoints. IEEE Transactions on Robotics. 39(5). 4131–4151. 15 indexed citations
8.
Chang, Yun, Kamak Ebadi, Christopher E. Denniston, et al.. (2022). LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments. IEEE Robotics and Automation Letters. 7(4). 9175–9182. 99 indexed citations
9.
Carlone, Luca, Kasra Khosoussi, Vasileios Tzoumas, et al.. (2022). Visual Navigation for Autonomous Vehicles: An Open-source Hands-on Robotics Course at MIT. 177–184. 3 indexed citations
10.
Shi, Jingnan, Heng Yang, & Luca Carlone. (2021). Optimal Pose and Shape Estimation for Category-level 3D Object Perception. 10 indexed citations
11.
Shi, Jingnan, Heng Yang, & Luca Carlone. (2021). ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants. 13820–13827. 43 indexed citations
12.
Yang, Heng, Jingnan Shi, & Luca Carlone. (2020). TEASER: Fast and Certifiable Point Cloud Registration. IEEE Transactions on Robotics. 37(2). 314–333. 502 indexed citations breakdown →

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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