Name
Abstract | ||
|---|---|---|
In recent years, digital reconstruction of cultural heritage provides an effective way of protecting historical relics, in which the modeling of surface reflection of historical heritage with high fidelity places a very important role. In this paper Gaussian process (GP) regression based approach is proposed to model the reflection properties of real materials, in which the simulation data generated by the existing model are both used as the training data and the proof that Gaussian process model can be used to describe the material reflection. Matusik's MERL database is also adopted to perform training and inference and obtain the reflection model of the real material. Simulation results show that the proposed GP regression approach can achieve a good fitting of the reflection properties of certain materials, greatly reduce the BRDF measurement time and ensure high realistic rendering at the same time. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-16631-5_40 | COMPUTER VISION - ACCV 2014 WORKSHOPS, PT II |
Field | DocType | Volume |
High fidelity,Kriging,Bidirectional reflectance distribution function,Covariance function,Pattern recognition,Cultural heritage,Computer science,Inference,Gaussian process,Artificial intelligence,Rendering (computer graphics) | Conference | 9009 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianying Hao | 1 | 0 | 0.34 |
| Yue Liu | 2 | 441 | 84.32 |
| Dongdong Weng | 3 | 29 | 19.16 |