Previously, I received my Master's degree in Computer Science (with distinction)
and my
Bachelor's degree in Software Engineering both at Graz
University of Technology between 2018 and 2023.
During this time, I worked as a study assistant, supporting various visual
computing
classes.
In 2019 & 2020, I was featured on the Dean's
List of my faculty (top 5% of students).
News
05/25:
I'm joining Meta Reality
Labs as a Research Scientist Intern!
I'm broadly interested in 3D, computer graphics, computer vision, machine
learning and
parallel
processing.
Most of my work focuses on editable, view-consistent radiance field
representations (e.g. NeRF or Gaussian Splatting).
We investigate a bidirectional reprojection method for challenging split
rendering scenarios,
and add a dynamic shadow encoding scheme and use auxiliary cameras to handle
occlusions.
We introduce a method for Anti-Aliased and
Artifact-Free (AAA)
Gaussian Rendering, building on per-pixel resorting,
view-adaptive filtering and view-space culling.
We combine StopThePop, Optimal Projection and Mini-Splatting with single-pass
foveated rendering for view-consistent real-time rendering of Gaussian Splats on
HMDs.
We leverage the architecture of Instant-NGP to cache view-independent latent
codes in a frustum
voxel grid for high-quality real-time and offline rendering.
Sorting 3D Gaussians along view rays leads to increased view-consistency.
With our hierarchical, GPU-friendly resorting scheme, our renderer is only 4%
slower than 3DGS
on average.
Locally Stylized Neural Radiance Fields via point-based 3D style transfer with
geometry-aware
losses - reduced background artefacts, more detail retention and
view-consistency.