| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0643909 |
| utime |
20260115100306.4 |
| mtime |
20260105235959.9 |
| title
(primary) (eng) |
Deep learning for predictive rendering of 3D printed objects |
| specification |
| page_count |
10 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0643904 |
| ISSN |
1613-0073 |
| title
|
Proceedings of the MANER Conference Mainz/Darmstadt 2025 (MANER 2025) |
| publisher |
| place |
Germany |
| name |
CEUR-WS |
| year |
2025 |
|
| editor |
| name1 |
Urban |
| name2 |
Philipp |
|
| editor |
| name1 |
von Castell |
| name2 |
Christoph Freiherr |
|
| editor |
| name1 |
Hardeberg |
| name2 |
Jon Yngve |
|
| editor |
| name1 |
Fleming |
| name2 |
Roland W. |
|
| editor |
| name1 |
Gigilashvili |
| name2 |
Davit |
|
|
| keyword |
Deep Learning |
| keyword |
Computer Graphics |
| keyword |
Rendering |
| author
(primary) |
| ARLID |
cav_un_auth*0500293 |
| name1 |
Amanturdieva |
| name2 |
A. |
| country |
NO |
| share |
50 |
| garant |
K |
|
| author
|
| ARLID |
cav_un_auth*0500294 |
| name1 |
Gigilashvili |
| name2 |
D. |
| country |
NO |
| share |
25 |
|
| author
|
| ARLID |
cav_un_auth*0101086 |
| name1 |
Filip |
| name2 |
Jiří |
| 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 |
| share |
25 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA22-17529S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0439849 |
|
| abstract
(eng) |
This study explores the development of a deep learning-based predictive rendering system for 3D printed objects, addressing the challenge of accurately predicting surface appearance from input parameters like surface normals, light angles, view positions, and tangent vectors. By utilizing the Deep Shading architecture, we present and explore a method that synthesizes rendered appearances. The dataset, sourced from controlled multi-view and illumination imaging conditions, serves as the foundation for training and evaluating the model. We tested various loss functions and training data demonstrating a promising performance in 3D printed appearance reproduction. Our findings contribute to the broader effort of improving predictive rendering systems for 3D printed objects, with potential applications in manufacturing, design, and material science. |
| action |
| ARLID |
cav_un_auth*0500280 |
| name |
MANER 2025 |
| dates |
20250629 |
| mrcbC20-s |
20250629 |
| place |
Mainz/Darmstadt |
| country |
DE |
|
| RIV |
IN |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20201 |
| reportyear |
2026 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0374429 |
| cooperation |
| ARLID |
cav_un_auth*0402682 |
| name |
Norges teknisk-naturvitenskapelige universitet, Trondheim |
| institution |
NTNU |
| country |
NO |
|
| confidential |
S |
| article_num |
6 |
| arlyear |
2025 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0643904 Proceedings of the MANER Conference Mainz/Darmstadt 2025 (MANER 2025) 1613-0073 0074-4135 Germany CEUR-WS 2025 Vol-4135 CEUR Workshop Proceedings 4135 |
| mrcbU67 |
Urban Philipp 340 |
| mrcbU67 |
von Castell Christoph Freiherr 340 |
| mrcbU67 |
Hardeberg Jon Yngve 340 |
| mrcbU67 |
Fleming Roland W. 340 |
| mrcbU67 |
Gigilashvili Davit 340 |
|