Název:	Particle-Based Fluid Surface Rendering with Neural Networks
Autoři:	Burkus, Viktória Kárpáti, Attila Szécsi, László
Citace zdrojového dokumentu:	WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 237-244.
Datum vydání:	2021
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	conferenceObject konferenční příspěvek
URI:	http://hdl.handle.net/11025/45029
ISBN:	978-80-86943-34-3
ISSN:	2464-4617 2464–4625(CD/DVD)
Klíčová slova:	počítačová grafika;metaballs;generativní neurální síť;rekonstrukce povrchu
Klíčová slova v dalším jazyce:	computer graphics;metaballs;generative neural network;surface reconstruction
Abstrakt v dalším jazyce:	Surface reconstruction for particle-based fluid simulation is a computational challenge on par with the simula-tion itself. In real-time applications, splatting-style rendering approaches based on forward rendering of particleimpostors are prevalent, but they suffer from noticeable artifacts.In this paper, we present a technique that combines forward rendering simulated features with deep-learning imagemanipulation to improve the rendering quality of splatting-style approaches to be perceptually similar to ray tracingsolutions, circumventing the cost, complexity, and limitations of exact fluid surface rendering by replacing it withthe flat cost of a neural network pass. Our solution is based on the idea of training generative deep neural networkswith image pairs consisting of cheap particle impostor renders and ground truth high quality ray-traced images.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2021: Full Papers Proceedings

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.