Rainer, G;
Ghosh, A;
Jakob, W;
Weyrich, T;
(2020)
Unified Neural Encoding of BTFs.
Computer Graphics Forum
, 39
(2)
pp. 167-178.
10.1111/cgf.13921.
Preview |
Text
Weyrich_rainer2020unified.pdf - Published Version Download (28MB) | Preview |
Abstract
Realistic rendering using discrete reflectance measurements is challenging, because arbitrary directions on the light and view hemispheres are queried at render time, incurring large memory requirements and the need for interpolation. This explains the desire for compact and continuously parametrized models akin to analytic BRDFs; however, fitting BRDF parameters to complex data such as BTF texels can prove challenging, as models tend to describe restricted function spaces that cannot encompass real‐world behavior. Recent advances in this area have increasingly relied on neural representations that are trained to reproduce acquired reflectance data. The associated training process is extremely costly and must typically be repeated for each material. Inspired by autoencoders, we propose a unified network architecture that is trained on a variety of materials, and which projects reflectance measurements to a shared latent parameter space. Similarly to SVBRDF fitting, real‐world materials are represented by parameter maps, and the decoder network is analog to the analytic BRDF expression (also parametrized on light and view directions for practical rendering application). With this approach, encoding and decoding materials becomes a simple matter of evaluating the network. We train and validate on BTF datasets of the University of Bonn, but there are no prerequisites on either the number of angular reflectance samples, or the sample positions. Additionally, we show that the latent space is well‐behaved and can be sampled from, for applications such as mipmapping and texture synthesis.
| Type: | Article |
|---|---|
| Title: | Unified Neural Encoding of BTFs |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/cgf.13921 |
| Publisher version: | https://doi.org/10.1111/cgf.13921 |
| Language: | English |
| Additional information: | Except where otherwise noted, this item's license is described as Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). |
| 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.ucl.ac.uk/id/eprint/10094250 |
Archive Staff Only
 |
View Item |
