Open AccessParticle-Based Fluid Surface Rendering with Neural Networks
Author(s) -
Viktória Burkus,
A. Kárpáti,
László Szécsi
Publication year - 2021
Publication title -
computer science research notes
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.11
H-Index - 4
eISSN - 2464-4625
pISSN - 2464-4617
DOI - 10.24132/csrn.2021.3101.26
Subject(s) - rendering (computer graphics) , computer science , artificial intelligence , 3d rendering , artificial neural network , fluid simulation , computer graphics (images) , ray tracing (physics) , deep learning , computer vision , tiled rendering , real time rendering , particle system , deep neural networks , distributed ray tracing , computer graphics , software rendering , 3d computer graphics , physics , quantum mechanics , mechanics
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 particle impostors are prevalent, but they suffer from noticeable artifacts. In this paper, we present a technique that combines forward rendering simulated features with deep-learning image manipulation to improve the rendering quality of splatting-style approaches to be perceptually similar to ray tracing solutions, circumventing the cost, complexity, and limitations of exact fluid surface rendering by replacing it with the flat cost of a neural network pass. Our solution is based on the idea of training generative deep neural networks with image pairs consisting of cheap particle impostor renders and ground truth high quality ray-traced images.