A Compact Representation of Measured BRDFs Using Neural Processes

Citations Over TimeTop 10% of 2021 papers

Abstract

In this article, we introduce a compact representation for measured BRDFs by leveraging Neural Processes (NPs). Unlike prior methods that express those BRDFs as discrete high-dimensional matrices or tensors, our technique considers measured BRDFs as continuous functions and works in corresponding function spaces . Specifically, provided the evaluations of a set of BRDFs, such as ones in MERL and EPFL datasets, our method learns a low-dimensional latent space as well as a few neural networks to encode and decode these measured BRDFs or new BRDFs into and from this space in a non-linear fashion. Leveraging this latent space and the flexibility offered by the NPs formulation, our encoded BRDFs are highly compact and offer a level of accuracy better than prior methods. We demonstrate the practical usefulness of our approach via two important applications, BRDF compression and editing. Additionally, we design two alternative post-trained decoders to, respectively, achieve better compression ratio for individual BRDFs and enable importance sampling of BRDFs.

Related Papers

• → New developments on the Encyclopedia of DNA Elements (ENCODE) data portal(2019)1,073 cited
• → Using the ENCODE Resource for Functional Annotation of Genetic Variants(2015)32 cited
• → Extrapolating ENCODE data to the whole human genome(2008)2 cited
• The Study of OCDMA Encode/Decode Using Multilayer Films(2001)
• The ENCODE Project Decoded(2012)