Liu, Chen;
Fischer, Michael;
Ritschel, Tobias;
(2023)
Learning to Learn and Sample BRDFs.
Computer Graphics Forum
, 42
(2)
pp. 201-211.
10.1111/cgf.14754.
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Abstract
We propose a method to accelerate the joint process of physically acquiring and learning neural Bi‐directional Reflectance Distribution Function (BRDF) models. While BRDF learning alone can be accelerated by meta‐learning, acquisition remains slow as it relies on a mechanical process. We show that meta‐learning can be extended to optimize the physical sampling pattern, too. After our method has been meta‐trained for a set of fully‐sampled BRDFs, it is able to quickly train on new BRDFs with up to five orders of magnitude fewer physical acquisition samples at similar quality. Our approach also extends to other linear and non‐linear BRDF models, which we show in an extensive evaluation.
| Type: | Article |
|---|---|
| Title: | Learning to Learn and Sample BRDFs |
| Location: | GERMANY, Saarbrucken |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/cgf.14754 |
| Publisher version: | http://dx.doi.org/10.1111/cgf.14754 |
| Language: | English |
| Additional information: | © 2023 The Authors. Computer Graphics Forum published by Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Science & Technology, Technology, Computer Science, Software Engineering, Computer Science, ILLUMINATION, REFLECTANCE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10192336 |
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