Name
Abstract | ||
|---|---|---|
In this paper we introduce unique publicly available dense an isotropic BRDF data measurements. We use this dense data as a reference for performance evaluation of the proposed BRDF sparse angular sampling and interpolation approach. The method is based on sampling of BRDF subspaces at fixed elevations by means of several adaptively-represented, uniformly distributed, perpendicular slices. Although this proposed method requires only a sparse sampling of material, the interpolation provides a very accurate reconstruction, visually and computationally comparable to densely measured reference. Due to the simple slices measurement and method's robustness it allows for a highly accurate acquisition of BRDFs. This in comparison with standard uniform angular sampling, is considerably faster yet uses far less samples. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CVPR.2013.193 | Computer Vision and Pattern Recognition |
Keywords | Field | DocType |
data acquisition,image reconstruction,image sampling,BRDF slices,BRDF sparse angular sampling,adaptive anisotropic appearance acquisition,interpolation approach,isotropic BRDF data measurements,performance evaluation,reconstruction,BRDF,adaptive,measurement,sampling | Iterative reconstruction,Bidirectional reflectance distribution function,Isotropy,Computer vision,Anisotropy,Computer science,Data acquisition,Interpolation,Robustness (computer science),Sampling (statistics),Artificial intelligence | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1063-6919 |
Citations | PageRank | References |
11 | 0.57 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jirí Filip | 1 | 103 | 12.96 |
| Radomír Vávra | 2 | 21 | 5.12 |
| Michal Haindl | 3 | 488 | 50.33 |
| Id, P. | 4 | 11 | 0.57 |