| Title: | Particle-Based Fluid Surface Rendering with Neural Networks |
| Authors: | Burkus, Viktória Kárpáti, Attila Szécsi, László |
| Citation: | WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 237-244. |
| Issue Date: | 2021 |
| Publisher: | Václav Skala - UNION Agency |
| Document type: | conferenceObject konferenční příspěvek |
| URI: | http://hdl.handle.net/11025/45029 |
| ISBN: | 978-80-86943-34-3 |
| ISSN: | 2464-4617 2464–4625(CD/DVD) |
| Keywords: | počítačová grafika;metaballs;generativní neurální síť;rekonstrukce povrchu |
| Keywords in different language: | computer graphics;metaballs;generative neural network;surface reconstruction |
| Abstract in different language: | 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. |
| Rights: | © Václav Skala - UNION Agency |
| Appears in Collections: | WSCG 2021: Full Papers Proceedings |
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