| bibtype |
J -
Journal Article
|
| ARLID |
0463872 |
| utime |
20240111140926.1 |
| mtime |
20161017235959.9 |
| SCOPUS |
84992640392 |
| WOS |
000388497400031 |
| DOI |
10.1111/cgf.13027 |
| title
(primary) (eng) |
Minimal Sampling for Effective Acquisition of Anisotropic BRDFs |
| specification |
| page_count |
11 s. |
| media_type |
C |
|
| serial |
| ARLID |
cav_un_epca*0252549 |
| ISSN |
0167-7055 |
| title
|
Computer Graphics Forum |
| volume_id |
35 |
| volume |
7 (2016) |
| page_num |
299-309 |
| publisher |
|
|
| keyword |
BRDF |
| keyword |
anisotropic |
| keyword |
measurement |
| author
(primary) |
| ARLID |
cav_un_auth*0282273 |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| full_dept |
Department of Pattern Recognition |
| share |
50 |
| name1 |
Vávra |
| name2 |
Radomír |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101086 |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| full_dept |
Department of Pattern Recognition |
| share |
50 |
| name1 |
Filip |
| name2 |
Jiří |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0303412 |
| project_id |
GA14-02652S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
BRDFs are commonly used for material appearance representation in applications ranging from gaming and the movie industry, to product design and specification. Most applications rely on isotropic BRDFs due to their better availability as a result of their easier acquisition process. On the other hand, anisotropic BRDF due to their structure-dependent anisotropic highlights, are more challenging to measure and process. This paper thus leverages the measurement process of anisotropic BRDF by representing such BRDF by the collection of isotropic BRDFs. Our method relies on an anisotropic BRDF database decomposition into training isotropic slices forming a linear basis, where appropriate sparse samples are identified using numerical optimization. When an unknown anisotropic BRDF is measured, these samples are repeatably captured in a small set of azimuthal directions. |
| RIV |
BD |
| reportyear |
2017 |
| num_of_auth |
2 |
| mrcbC52 |
4 A hod 4ah 20231122141928.3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0263660 |
| mrcbC64 |
1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
| confidential |
S |
| mrcbC86 |
1 Article|Proceedings Paper Computer Science Software Engineering |
| mrcbT16-e |
COMPUTERSCIENCESOFTWAREENGINEERING |
| mrcbT16-j |
1.174 |
| mrcbT16-s |
0.733 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
88.573 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1 |
| arlyear |
2016 |
| mrcbTft |
\nSoubory v repozitáři: vavra-0463872.pdf |
| mrcbU14 |
84992640392 SCOPUS |
| mrcbU34 |
000388497400031 WOS |
| mrcbU56 |
14MB |
| mrcbU63 |
cav_un_epca*0252549 Computer Graphics Forum 0167-7055 1467-8659 Roč. 35 č. 7 2016 299 309 Wiley |
|