bibtype |
C -
Conference Paper (international conference)
|
ARLID |
0569632 |
utime |
20240402213643.4 |
mtime |
20230306235959.9 |
DOI |
10.5220/0011616200003417 |
title
(primary) (eng) |
Optimal Activation Function for Anisotropic BRDF Modeling |
specification |
page_count |
8 s. |
media_type |
P |
|
serial |
ARLID |
cav_un_epca*0569631 |
ISBN |
978-989-758-634-7 |
ISSN |
2184-4321 |
title
|
Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP |
page_num |
162-169 |
publisher |
place |
Lisbon |
name |
SciTePress |
year |
2023 |
|
editor |
name1 |
Sousa |
name2 |
A. Augusto |
|
editor |
name1 |
Bashford-Rogers |
name2 |
Thomas |
|
editor |
name1 |
Bouatouch |
name2 |
Kadi |
|
|
keyword |
Anisotropic BRDF models |
keyword |
neural network |
keyword |
activation function |
keyword |
BTF |
author
(primary) |
ARLID |
cav_un_auth*0101165 |
name1 |
Mikeš |
name2 |
Stanislav |
institution |
UTIA-B |
full_dept (cz) |
Rozpoznávání obrazu |
full_dept (eng) |
Department of Pattern Recognition |
department (cz) |
RO |
department (eng) |
RO |
fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
author
|
ARLID |
cav_un_auth*0101093 |
name1 |
Haindl |
name2 |
Michal |
institution |
UTIA-B |
full_dept (cz) |
Rozpoznávání obrazu |
full_dept |
Department of Pattern Recognition |
department (cz) |
RO |
department |
RO |
full_dept |
Department of Pattern Recognition |
fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
source |
|
cas_special |
project |
project_id |
GA19-12340S |
agency |
GA ČR |
country |
CZ |
ARLID |
cav_un_auth*0376011 |
|
abstract
(eng) |
We present simple and fast neural anisotropic Bidirectional Reflectance Distribution Function (NN-BRDF) efficient models, capable of accurately estimating unmeasured combinations of illumination and viewing angles from sparse Bidirectional Texture Function (BTF) measurement of neighboring points in the illumination/viewing hemisphere. Our models are optimized for the best-performing activation function from nineteen widely used nonlinear functions and can be directly used in rendering. We demonstrate that the activation function significantly influences the modeling precision. The models enable us to reach significant time and cost-saving in not trivial and costly BTF measurements while maintaining acceptably low modeling error. The presented models learn well, even from only three percent of the original BTF measurements, and we can prove this by precise evaluation of the modeling error, which is smaller than the errors of alternative analytical BRDF models. |
action |
ARLID |
cav_un_auth*0446965 |
name |
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP 2023 /18./ |
dates |
20230219 |
mrcbC20-s |
20230221 |
place |
Lisbon |
country |
PT |
|
RIV |
BD |
FORD0 |
20000 |
FORD1 |
20200 |
FORD2 |
20205 |
reportyear |
2024 |
num_of_auth |
2 |
presentation_type |
PR |
inst_support |
RVO:67985556 |
permalink |
https://hdl.handle.net/11104/0341255 |
confidential |
S |
arlyear |
2023 |
mrcbU14 |
SCOPUS |
mrcbU24 |
PUBMED |
mrcbU34 |
WOS |
mrcbU63 |
cav_un_epca*0569631 Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP SciTePress 2023 Lisbon 162 169 978-989-758-634-7 2184-4321 |
mrcbU67 |
Sousa A. Augusto 340 |
mrcbU67 |
Bashford-Rogers Thomas 340 |
mrcbU67 |
Bouatouch Kadi 340 |
|