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
eISSN - 2464-4625
pISSN - 2464-4617
DOI - 10.24132/csrn.2021.3002.26
Subject(s) - rendering (computer graphics) , computer science , artificial intelligence , fluid simulation , artificial neural network , 3d rendering , computer graphics (images) , deep learning , real time rendering , particle system , deep neural networks , computer vision , physics , 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 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.