Name
Abstract | ||
|---|---|---|
Many applications, such as computer-aided design and game rendering, need to reproduce realistic material appearance in complex light environment and different visual conditions. The authenticity of the three-dimensional object or the scene is heavily depended on the representation and rendering of textures, where the Bidirectional Texture Function (BTF) is one of the most widely-used texture models. In this paper, we proposed a neural network to learn the representation of the BTF data for predicting new texture images under novel conditions. The proposed method was tested on a public BTF dataset and was shown to produce satisfactory synthetic results. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.procs.2019.01.241 | Procedia Computer Science |
Keywords | Field | DocType |
Bidirectional Texture Function (BTF),image generation,Neural Networks,image compression,Adversarial Training | Computer vision,Computer science,Bidirectional texture function,Artificial intelligence,Deep learning,Rendering (computer graphics),Artificial neural network,Test data generation,Machine learning | Conference |
Volume | ISSN | Citations |
147 | 1877-0509 | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| xiaohua zhang | 1 | 2 | 4.48 |
| Junyu Dong | 2 | 393 | 77.68 |
| Yanhai Gan | 3 | 2 | 2.06 |
| Hui Yu | 4 | 128 | 21.50 |
| Lin Qi | 5 | 27 | 8.68 |