About

Pascal Fua is a prominent computer vision researcher whose work spans 3D object tracking, deformable shape reconstruction, robotic perception, and medical image analysis. Based at EPFL, he has made foundational contributions to some of the field's most challenging problems, consistently bridging theoretical rigor with practical application. Fua's most celebrated contribution is his comprehensive survey on monocular model-based 3D tracking of rigid objects (2005), which has accumulated over 500 citations and remains an essential reference for researchers in augmented reality, robotics, and human-computer interaction. His early work on articulated soft objects — combining implicit surfaces with robotics-inspired articulated frameworks for human body modeling — laid important groundwork for video-based motion capture, earning over 270 combined citations across two landmark papers. Beyond rigid and articulated structures, Fua has pushed boundaries in non-rigid 3D reconstruction, view-based robotic mapping, and specialized domains such as retinal microsurgery tracking, demonstrating the real-world clinical relevance of his methods. His 2023 state-of-the-art survey on dense monocular non-rigid reconstruction reflects his continued leadership in an evolving field. With a career spanning nearly three decades and hundreds of highly cited publications, Pascal Fua stands as one of computer vision's most versatile and enduring contributors.

Research Focus

Key Achievements

12
H-Index
16
Papers
1,762
Total Citations
110
Avg Citations/Paper
🏆 Most Cited Paper
Monocular Model-Based 3D Tracking of Rigid Objects: A Survey
531 citations · 2005
📈 Most Prolific Year: 2005 (2 Papers)
🤝 Key Collaborators: 40
🏛 Institutions: University of Lausanne, École Polytechnique Fédérale de Lausanne, SRI International, Institut national de recherche en sciences et technologies du numérique

Top Papers

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    View-based Maps
    224 citations · 2010
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    View-based maps
    33 citations · 2009
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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago