2024W

• Fundamentals of Computer Graphics / 193.018 / VU

• Gaussian Mixture Convolution Networks / Celarek, A., Hermosilla Casajus, P., Kerbl, B., Ropinski, T., & Wimmer, M. (2022). Gaussian Mixture Convolution Networks. In The Tenth International Conference on Learning Representations (ICLR 2022). The Tenth International Conference on Learning Representations, ICLR 2022, Unknown. https://doi.org/10.34726/4801
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  Project: EVOCATION (2018–2022)
• Quantifying the Error of Light Transport Algorithms / Celarek, A., Wenzel, J., Wimmer, M., & Lehtinen, J. (2019). Quantifying the Error of Light Transport Algorithms. Computer Graphics Forum, 38(4), 111–121. https://doi.org/10.1111/cgf.13775
  Download: PDF (2.65 MB)
  Project: EVOCATION (2018–2022)
• Quantifying the convergence of light transport algorithms / Celarek, A. (2017). Quantifying the convergence of light transport algorithms [Diploma Thesis, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/79917

• Real-time avalanche risk visualization on a large-scale geospatial dataset / Eschner, J. (2023). Real-time avalanche risk visualization on a large-scale geospatial dataset [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.102961
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• Construction and visualization of Gaussian mixture models from point clouds for 3D object representation / Fraiss, S. M. (2022). Construction and visualization of Gaussian mixture models from point clouds for 3D object representation [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.82630
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