Rafał Mantiuk *
Rafał Mantiuk
Professor of Graphics and Displays
Department of Computer Science and Technology
The Computer Laboratory
Rainbow Research Group
University of Cambridge

Office address
University of Cambridge
Computer Laboratory
William Gates Building
15 JJ Thomson Avenue
Cambridge CB3 0FD
United Kingdom
office: +44 1223 763831
rafal [dot] mantiuk [at] cl [dot] cam [dot] ac [dot] uk

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Reserach interest

Applied visual perception; high dynamic range imaging; display algorithms; machine learning for image synthesis; tone-mapping; video coding for new display technologies; image and video quality metrics; visibility metrics; virtual reality and low-level perception; computational photography; computational displays; novel display technologies; colour; perception in computer graphics; novel image and video representations (beyond 2D); psychophysics; modeling visual perception with machine learning.


Professor/Reader of Graphics and Displays, University of Cambridge, Computer Laboratory, UK (from 2018)
Senior Lecturer, University of Cambridge, Computer Laboratory, UK (2015-2018)
Lecturer/Senior Lecturer, Bangor University, School of Computer Science, UK (2009-2015)
Postdoc Fellow, University of British Columbia, Canada (2008-2009)
Postdoc, Max-Planck-Institut for Computer Science, Germany (2007-2008)
Internship, Sharp Laboratories of America, Camas WA, USA (2006)
PhD (summa cum laude, Computer Science), Max-Planck-Institut for Computer Science, Germany (2006)
Msc (Computer Science), Technical University of Szczecin, Poland (2003)
Google Scholar LinkedIn profile Mendeley profile

Recent projects

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The effect of display capabilities on the gloss consistency between real and virtual objects

We reproduce gloss on our ultra-realistic HDR 3D display so that it appears identical to the gloss of real objects seen side by side. Our observation is that the dynamic range, absolute luminance and tone-curve are the factors that influence gloss perception the most.

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Neural Partitioning Pyramids for Denoising Monte Carlo Renderings

Spatiotemporal denoising for path tracing relies on a trained multi-scale decomposition (pyramid), which can better preserve details and avoid artifacts of previous methods.

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Robust estimation of exposure ratios in multi-exposure image stacks

Inaccurate information on exposure times result in banding artifacts when merging a stack of multiple exposures into an HDR image. We show how the exposure times can be robustly estimated from an exposure stack while accounting for camera noise and pixel misalignment.

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Recent papers

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Awards and grants

My contribution to the organization of research networks and conferences can be found here.