Name
Abstract | ||
|---|---|---|
AbstractIn 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3490385 | ACM Transactions on Graphics |
Keywords | DocType | Volume |
Neural Processes, BRDF | Journal | 41 |
Issue | ISSN | Citations |
2 | 0730-0301 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuankun Zheng | 1 | 0 | 0.34 |
| Ruzhang Zheng | 2 | 0 | 0.34 |
| Rui Wang | 3 | 489 | 33.21 |
| Shuang Zhao | 4 | 358 | 26.74 |
| Hujun Bao | 5 | 2801 | 174.65 |