Diploma and Master Theses (authored and supervised):

R. Horvath:
"Image-Space Metaballs Using Deep Learning";
Supervisor: I. Viola; Visual Computing and Human-Centered Technology, 2019; final examination: 2019-07-24.

English abstract:
Metaballs are a type of implicit surface that are used to model organic-looking shapes and fluids. Accurate rendering of three-dimensional metaballs is typically done using ray-casting, which is computationally expensive and not suitable for real-time applications, therefore di˙erent approximate methods for rendering metaballs have been developed. In this thesis, the foundations of metaballs and neural networks are discussed, and a new approach to rendering metaballs using Deep Learning that is fast enough for use in real-time applications is presented. The system uses an image-to-image translation approach. For that, first the metaballs are rendered using a very simplified representation to an image. This image is then used as input to a neural network that outputs a depth, normal and base color bu˙er that can be combined using a deferred shading renderer to produce a final image.

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.