Simon Hadfield

4.9k total citations · 1 hit paper
42 papers, 1.3k citations indexed

About

Simon Hadfield is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Human-Computer Interaction. According to data from OpenAlex, Simon Hadfield has authored 42 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 16 papers in Aerospace Engineering and 7 papers in Human-Computer Interaction. Recurrent topics in Simon Hadfield's work include Robotics and Sensor-Based Localization (13 papers), Advanced Vision and Imaging (12 papers) and Human Pose and Action Recognition (11 papers). Simon Hadfield is often cited by papers focused on Robotics and Sensor-Based Localization (13 papers), Advanced Vision and Imaging (12 papers) and Human Pose and Action Recognition (11 papers). Simon Hadfield collaborates with scholars based in United Kingdom, United States and Germany. Simon Hadfield's co-authors include Richard Bowden, Necati Cihan Camgöz, Oscar Koller, Hermann Ney, Karel Lebeda, Jiřı́ Matas, Nicolas Pugeault, Miguel López‐Benítez, Timothy D. Drysdale and Christopher Bridges and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and International Journal of Computer Vision.

In The Last Decade

Simon Hadfield

38 papers receiving 1.2k 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.

Neural Sign Language Translation

2018 402 citations Necati Cihan Camgöz, Simon Hadfield et al. View profile →

Peers

Simon Hadfield

HI

CVPR

DEP

BE

AI

David Demirdjian United States

Karl–Friedrich Kraiss Germany

Tamer Shanableh United Arab Emirates

Dongxu Li Australia

Sylvie C. W. Ong Singapore

Jonathan Alon United States

Joyeeta Singha India

Necati Cihan Camgöz United Kingdom

Surendra Ranganath Singapore

David Demirdjian United States

Simon Hadfield

432 ×0.5

HI

739 ×0.9

CVPR

73 ×0.1

DEP

144 ×0.5

BE

216 ×1.3

AI

Citations per year, relative to Simon Hadfield Simon Hadfield (= 1×) peers David Demirdjian

Countries citing papers authored by Simon Hadfield

Since Specialization

Citations

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

Fields of papers citing papers by Simon Hadfield

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon Hadfield

This figure shows the co-authorship network connecting the top 25 collaborators of Simon Hadfield. A scholar is included among the top collaborators of Simon Hadfield 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 Simon Hadfield. Simon Hadfield 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.

Hadfield, Simon, et al.. (2025). HyperGS: Hyperspectral 3D Gaussian Splatting. 5970–5979. 1 indexed citations

2.

Hadfield, Simon, et al.. (2025). PEnG: Pose-Enhanced Geo-Localisation. IEEE Robotics and Automation Letters. 10(4). 3835–3842.

3.

Hadfield, Simon, et al.. (2025). SpaGBOL: Spatial-Graph-Based Orientated Localisation. 6858–6867.

4.

Hadfield, Simon, et al.. (2024). BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation. 11048–11055. 2 indexed citations

5.

Mustafa, Armin, et al.. (2024). S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal. View. 5898–5908. 2 indexed citations

6.

Davidson, Rebecca M., et al.. (2023). The Development of an Onboard Processing Environment within the Flexible and Intelligent Payload Chain Sub-system for Small EO Satellites. View. 1–8.

7.

Spencer, Jaime, Cheng Qian, Chris Russell, et al.. (2023). The Monocular Depth Estimation Challenge. ePrints Soton (University of Southampton). 623–632. 9 indexed citations

8.

Hadfield, Simon, et al.. (2022). ASL-SLAM: An Asynchronous Formulation of Lines for SLAM with Event Sensors. View. 84–91. 2 indexed citations

9.

Hadfield, Simon, et al.. (2022). SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 7060–7065. 3 indexed citations

10.

Hadfield, Simon, et al.. (2021). HARL-A: Hardware Agnostic Reinforcement Learning Through Adversarial Selection. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 3499–3505. 1 indexed citations

11.

Hadfield, Simon, et al.. (2021). Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour. 5959–5965. 2 indexed citations

12.

Hadfield, Simon, et al.. (2019). SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation. International Journal of Computer Vision. 128(5). 1286–1310. 8 indexed citations

13.

Bridges, Christopher, et al.. (2019). Rapid Prototyping of Deep Learning Models on Radiation Hardened CPUs. View. 25–32. 9 indexed citations

14.

Camgöz, Necati Cihan, et al.. (2018). Sign Language Production using Neural Machine Translation and Generative Adversarial Networks. Surrey Research Insight Open Access (The University of Surrey). 304. 32 indexed citations

15.

Ebling, Sarah, Necati Cihan Camgöz, Penny Boyes Braem, et al.. (2018). SMILE Swiss German Sign Language Dataset. Language Resources and Evaluation. 18 indexed citations

16.

Camgöz, Necati Cihan, Simon Hadfield, Oscar Koller, Hermann Ney, & Richard Bowden. (2018). Neural Sign Language Translation. View. 7784–7793. 402 indexed citations breakdown →

17.

Camgöz, Necati Cihan, Simon Hadfield, & Richard Bowden. (2017). Particle Filter Based Probabilistic Forced Alignment for Continuous Gesture Recognition. View. 3079–3085. 8 indexed citations

18.

Hadfield, Simon, Karel Lebeda, & Richard Bowden. (2016). Hollywood 3D: What are the Best 3D Features for Action Recognition?. International Journal of Computer Vision. 121(1). 95–110. 13 indexed citations

19.

Hadfield, Simon & Richard Bowden. (2013). Scene Particles: Unregularized Particle-Based Scene Flow Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(3). 564–576. 31 indexed citations

20.

Hadfield, Simon & Richard Bowden. (2012). Image Analysis and Recognition. Lecture notes in computer science. 16 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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