Michael Steiner

I am currently a PhD student at Graz University of Technology at the Institute of Computer Graphics and Vision, supervised by Markus Steinberger. Most of my recent work focused on novel view synthesis and neural rendering. My personal research interests lie in Visual Computing, Machine Learning, and all topics concerning Parallel Compute in general.

In 2023 I received my Master's Degree (with distiction) for Computer Science at Graz University of Technology, with a Major/Minor in Visual Computing/Machine Learning. My Master Thesis tackled accelerated NeRF rendering on the basis of DONeRF.

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StopThePop: Sorted Gaussian Splatting for View-Consistent Real-Time Rendering
Michael Steiner*, Lukas Radl*, Mathias Parger, Alexander Weinrauch, Bernhard Kerbl, Markus Steinberger.
SIGGRAPH (TOG), 2024.

3D Gaussian Splatting performs an approximate global sort of primitives, leading to undesireable popping artifacts. By hierarchically sorting primitives in a tile-based rasterizer, we allow for view-consistent rendering while maintaining real-time performance.

Frustum Volume Caching for Accelerated NeRF Rendering
Michael Steiner, Thomas Köhler, Lukas Radl, Markus Steinberger.
High-Performance Graphics (PACMCGIT), 2024.

We accelerate NeRF rendering of high-quality video sequences by caching and temporally reusing NeRF latent codes. Our frustum-aligned volumetric cache datastructure together with our novel view-dependent cone encoding allow for smaller latent codes and fast re-evaluation, leading to render speed-ups of up to 2x for our Instant-NGP based model.

LAENeRF: Local Appearance Editing of Neural Radiance Fields
Lukas Radl, Michael Steiner, Andreas Kurz, Markus Steinberger.
CVPR, 2024.

Locally Stylized Neural Radiance Fields via point-based 3D style transfer with geometry-aware losses - reduced background artefacts, more detail retention and view-consistency.

Analyzing the Internals of Neural Radiance Fields
Lukas Radl, Andreas Kurz, Michael Steiner, Markus Steinberger.
CVPR Workshop on Neural Rendering Intelligence, 2024.

Using density estimates derived from activations for inverse transform sampling in NeRFs allows for faster inference and comparable visual quality.

Thank you to Jon Barron for providing the public source code of his website.