Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Parger, Mathias"'
Autor:
Vainer, Shimon, Boss, Mark, Parger, Mathias, Kutsy, Konstantin, De Nigris, Dante, Rowles, Ciara, Perony, Nicolas, Donné, Simon
Graphics pipelines require physically-based rendering (PBR) materials, yet current 3D content generation approaches are built on RGB models. We propose to model the PBR image distribution directly, avoiding photometric inaccuracies in RGB generation
Externí odkaz:
http://arxiv.org/abs/2402.05919
Autor:
Radl, Lukas, Steiner, Michael, Parger, Mathias, Weinrauch, Alexander, Kerbl, Bernhard, Steinberger, Markus
Publikováno v:
ACM Transactions on Graphics, volume 43(4), July 2024
Gaussian Splatting has emerged as a prominent model for constructing 3D representations from images across diverse domains. However, the efficiency of the 3D Gaussian Splatting rendering pipeline relies on several simplifications. Notably, reducing G
Externí odkaz:
http://arxiv.org/abs/2402.00525
Autor:
Parger, Mathias, Ertl, Alexander, Eibensteiner, Paul, Mueller, Joerg H., Winter, Martin, Steinberger, Markus
Training a sparse neural network from scratch requires optimizing connections at the same time as the weights themselves. Typically, the weights are redistributed after a predefined number of weight updates, removing a fraction of the parameters of e
Externí odkaz:
http://arxiv.org/abs/2210.14012
Autor:
Parger, Mathias, Tang, Chengcheng, Neff, Thomas, Twigg, Christopher D., Keskin, Cem, Wang, Robert, Steinberger, Markus
Convolutional neural network inference on video input is computationally expensive and requires high memory bandwidth. Recently, DeltaCNN managed to reduce the cost by only processing pixels with significant updates over the previous frame. However,
Externí odkaz:
http://arxiv.org/abs/2210.09887
Autor:
Parger, Mathias, Tang, Chengcheng, Twigg, Christopher D., Keskin, Cem, Wang, Robert, Steinberger, Markus
Convolutional neural network inference on video data requires powerful hardware for real-time processing. Given the inherent coherence across consecutive frames, large parts of a video typically change little. By skipping identical image regions and
Externí odkaz:
http://arxiv.org/abs/2203.03996
Autor:
Neff, Thomas, Stadlbauer, Pascal, Parger, Mathias, Kurz, Andreas, Mueller, Joerg H., Chaitanya, Chakravarty R. Alla, Kaplanyan, Anton, Steinberger, Markus
Publikováno v:
Computer Graphics Forum Volume 40, Issue 4, 2021
The recent research explosion around implicit neural representations, such as NeRF, shows that there is immense potential for implicitly storing high-quality scene and lighting information in compact neural networks. However, one major limitation pre
Externí odkaz:
http://arxiv.org/abs/2103.03231
Autor:
Parger, Mathias, Tang, Chengcheng, Xu, Yuanlu, Twigg, Christopher, Tao, Lingling, Li, Yijing, Wang, Robert, Steinberger, Markus
Tracking body and hand motions in the 3D space is essential for social and self-presence in augmented and virtual environments. Unlike the popular 3D pose estimation setting, the problem is often formulated as inside-out tracking based on embodied pe
Externí odkaz:
http://arxiv.org/abs/2012.03680
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