Pascal Fua

56.7k total citations · 18 hit papers
398 papers, 29.8k citations indexed

About

Pascal Fua is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Computational Mechanics. According to data from OpenAlex, Pascal Fua has authored 398 papers receiving a total of 29.8k indexed citations (citations by other indexed papers that have themselves been cited), including 319 papers in Computer Vision and Pattern Recognition, 99 papers in Aerospace Engineering and 68 papers in Computational Mechanics. Recurrent topics in Pascal Fua's work include Advanced Vision and Imaging (152 papers), Robotics and Sensor-Based Localization (98 papers) and Advanced Image and Video Retrieval Techniques (72 papers). Pascal Fua is often cited by papers focused on Advanced Vision and Imaging (152 papers), Robotics and Sensor-Based Localization (98 papers) and Advanced Image and Video Retrieval Techniques (72 papers). Pascal Fua collaborates with scholars based in Switzerland, United States and France. Pascal Fua's co-authors include Vincent Lepetit, Aurélien Lucchi, Radhakrishna Achanta, Kevin Smith, Sabine Süsstrunk, Anil Shaji, Francesc Moreno-Noguer, Mathieu Salzmann, François Fleuret and Engin Tola and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Cell Biology and PLoS ONE.

In The Last Decade

Pascal Fua

389 papers receiving 28.4k citations

Hit Papers

SLIC Superpixels Compared to State-of-the-Art Superpixel ... 2005 2026 2012 2019 2012 2008 2009 2011 2017 2.0k 4.0k 6.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pascal Fua Switzerland 73 22.7k 8.7k 3.7k 3.2k 2.1k 398 29.8k
Takeo Kanade United States 102 37.6k 1.7× 8.3k 1.0× 4.0k 1.1× 5.3k 1.6× 3.6k 1.7× 607 48.3k
Jean Ponce United States 51 18.7k 0.8× 3.9k 0.5× 3.6k 1.0× 4.1k 1.3× 1.4k 0.7× 172 24.8k
Martial Hebert United States 73 16.5k 0.7× 7.1k 0.8× 4.3k 1.2× 1.5k 0.5× 1.2k 0.6× 380 22.4k
Richard Hartley Australia 55 20.9k 0.9× 11.0k 1.3× 1.4k 0.4× 3.1k 0.9× 805 0.4× 216 27.1k
Ian Reid United Kingdom 63 16.0k 0.7× 9.2k 1.1× 3.0k 0.8× 1.9k 0.6× 889 0.4× 260 20.4k
Berthold K. P. Horn United States 47 18.9k 0.8× 5.7k 0.7× 1.5k 0.4× 2.9k 0.9× 913 0.4× 157 27.3k
Andrew Fitzgibbon United Kingdom 52 13.9k 0.6× 6.1k 0.7× 1.3k 0.3× 1.2k 0.4× 1.6k 0.8× 157 18.2k
Vladlen Koltun United States 60 11.3k 0.5× 4.5k 0.5× 2.6k 0.7× 1.2k 0.4× 2.2k 1.0× 163 18.6k
Jiřı́ Matas Czechia 61 20.8k 0.9× 6.8k 0.8× 3.8k 1.0× 3.2k 1.0× 749 0.4× 244 25.8k
Roberto Cipolla United Kingdom 57 13.6k 0.6× 4.0k 0.5× 2.6k 0.7× 1.4k 0.4× 969 0.5× 322 17.1k

Countries citing papers authored by Pascal Fua

Since Specialization
Citations

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

Fields of papers citing papers by Pascal Fua

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pascal Fua

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

All Works

20 of 20 papers shown
1.
Koziński, Mateusz, Pascal Fua, Angela Koutsokera, et al.. (2025). Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT. Communications Medicine. 5(1). 18–18.
2.
Roy, Soumava Kumar, et al.. (2024). Occlusion Resilient 3D Human Pose Estimation. 1198–1207.
3.
Yang, Jiancheng, et al.. (2024). Efficient anatomical labeling of pulmonary tree structures via deep point-graph representation-based implicit fields. Medical Image Analysis. 99. 103367–103367. 3 indexed citations
4.
Remelli, Edoardo, et al.. (2024). DeepMesh: Differentiable Iso-Surface Extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(11). 7072–7087. 3 indexed citations
5.
Honari, Sina, et al.. (2023). Detecting Road Obstacles by Erasing Them. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(4). 2450–2460. 16 indexed citations
6.
Koziński, Mateusz, et al.. (2023). Persistent Homology With Improved Locality Information for More Effective Delineation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(8). 10588–10595. 5 indexed citations
7.
Vasu, Subeesh, et al.. (2022). HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). abs 1607 5695. 617–626. 5 indexed citations
8.
Hoyt, Alison M., Ale×ander R. Cobb, Mateusz Koziński, et al.. (2021). Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions. SHILAP Revista de lepidopterología. 2(1). 29 indexed citations
9.
Salzmann, Mathieu, et al.. (2020). Domain Adaptive Multibranch Networks. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 5 indexed citations
10.
Mehta, Dushyant, Oleksandr Sotnychenko, Franziska Mueller, et al.. (2019). XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera. MPG.PuRe (Max Planck Society). 14 indexed citations
11.
Ono, Y., Eduard Trulls, Pascal Fua, & Kwang Moo Yi. (2018). LF-Net: Learning Local Features from Images. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 31. 6234–6244. 87 indexed citations
12.
Calı, Corrado, Carlos Becker, Bohumil Maco, et al.. (2018). The effects of aging on neuropil structure in mouse somatosensory cortex—A 3D electron microscopy analysis of layer 1. PLoS ONE. 13(7). e0198131–e0198131. 45 indexed citations
13.
Konyushkova, Ksenia, Raphael Sznitman, & Pascal Fua. (2017). Learning Active Learning from Real and Synthetic Data.. arXiv (Cornell University). 4 indexed citations
14.
Rozantsev, Artem, et al.. (2016). Vision-based Unmanned Aerial Vehicle detection and tracking for sense and avoid systems. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1556–1561. 43 indexed citations
15.
Sznitman, Raphael, Aurélien Lucchi, Peter I. Frazier, Bruno Jedynak, & Pascal Fua. (2013). An Optimal Policy for Target Localization with Application to Electron Microscopy. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 28(1). 1–9. 16 indexed citations
16.
Salzmann, Mathieu, Julien Pilet, Slobodan Ilić, & Pascal Fua. (2007). Surface Deformation Models for Nonrigid 3D Shape Recovery. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(8). 1481–1487. 83 indexed citations
17.
Fua, Pascal, et al.. (2005). Monocular Model-based 3d Tracking of Rigid Objects (Foundations and Trends in Computer Graphics and Vision(R)). now publishers, Inc. eBooks. 23 indexed citations
18.
Lengagne, R., Pascal Fua, & Olivier Monga. (2000). 3--D Stereo Reconstruction of Human Faces driven by Differential Constraints Image and Vision Computing. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 19 indexed citations
19.
Fua, Pascal. (1991). Combining stereo and monocular information to compute dense depth maps that preserve depth discontinuities. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1292–1298. 79 indexed citations
20.
Fua, Pascal & Andrew J. Hanson. (1989). Objective functions for feature discrimination: theory. Journal of Religion and Health. 49(4). 443–460. 9 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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