Search Results for author: Mathias Parger

Found 8 papers, 2 papers with code

Collaborative Control for Geometry-Conditioned PBR Image Generation

no code implementations8 Feb 2024 Shimon Vainer, Mark Boss, Mathias Parger, Konstantin Kutsy, Dante De Nigris, Ciara Rowles, Nicolas Perony, Simon Donné

Graphics pipelines require physically-based rendering (PBR) materials, yet current 3D content generation approaches are built on RGB models.

Image Generation

StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering

1 code implementation1 Feb 2024 Lukas Radl, Michael Steiner, Mathias Parger, Alexander Weinrauch, Bernhard Kerbl, Markus Steinberger

Consequently, rendering performance is nearly doubled, making our approach 1. 6x faster than the original Gaussian Splatting, with a 50% reduction in memory requirements.

Novel View Synthesis

MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos with Spherical Buffers and Padded Convolutions

no code implementations ICCV 2023 Mathias Parger, Chengcheng Tang, Thomas Neff, Christopher D. Twigg, Cem Keskin, Robert Wang, Markus Steinberger

Moving cameras add new challenges in how to fuse newly unveiled image regions with already processed regions efficiently to minimize the update rate - without increasing memory overhead and without knowing the camera extrinsics of future frames.

Gradient-based Weight Density Balancing for Robust Dynamic Sparse Training

no code implementations25 Oct 2022 Mathias Parger, Alexander Ertl, Paul Eibensteiner, Joerg H. Mueller, Martin Winter, Markus Steinberger

Typically, the weights are redistributed after a predefined number of weight updates, removing a fraction of the parameters of each layer and inserting them at different locations in the same layers.

MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos

no code implementations18 Oct 2022 Mathias Parger, Chengcheng Tang, Thomas Neff, Christopher D. Twigg, Cem Keskin, Robert Wang, Markus Steinberger

Moving cameras add new challenges in how to fuse newly unveiled image regions with already processed regions efficiently to minimize the update rate - without increasing memory overhead and without knowing the camera extrinsics of future frames.

DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos

no code implementations CVPR 2022 Mathias Parger, Chengcheng Tang, Christopher D. Twigg, Cem Keskin, Robert Wang, Markus Steinberger

With DeltaCNN, we present a sparse convolutional neural network framework that enables sparse frame-by-frame updates to accelerate video inference in practice.

DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks

1 code implementation4 Mar 2021 Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H. Mueller, Chakravarty R. Alla Chaitanya, Anton Kaplanyan, Markus Steinberger

In this work, we bring compact neural representations closer to practical rendering of synthetic content in real-time applications, such as games and virtual reality.

NeRF

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