Deep learning for predictive rendering of 3D printed objects

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00643909" target="_blank" >RIV/67985556:_____/25:00643909 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Deep learning for predictive rendering of 3D printed objects
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/GA22-17529S" target="_blank" >GA22-17529S: Visual fingerprint of material appearance</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the MANER Conference Mainz/Darmstadt 2025 (MANER 2025)
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
10
Pages from-to
6
Publisher name
CEUR-WS
Place of publication
Germany
Event location
Mainz/Darmstadt
Event date
Jun 29, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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