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Item Rendering 2023 Symposium Track: Frontmatter(The Eurographics Association, 2023) Ritschel, Tobias; Weidlich, Andrea; Ritschel, Tobias; Weidlich, AndreaItem Pacific Graphics 2024 - CGF 43-7: Frontmatter(The Eurographics Association and John Wiley & Sons Ltd., 2024) Chen, Renjie; Ritschel, Tobias; Whiting, Emily; Chen, Renjie; Ritschel, Tobias; Whiting, EmilyItem Blue Noise Plots(The Eurographics Association and John Wiley & Sons Ltd., 2021) Onzenoodt, Christian van; Singh, Gurprit; Ropinski, Timo; Ritschel, Tobias; Mitra, Niloy and Viola, IvanWe propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often onedimensional strip plots are used to depict such data, one of their main problems is visual clutter which results from overlap. To reduce this overlap, jitter plots were introduced, whereby an additional, non-encoding plot dimension is introduced, along which the data point representing dots are randomly perturbed. Unfortunately, this randomness can suggest non-existent clusters, and often leads to visually unappealing plots, in which overlap might still occur. To overcome these shortcomings, we introduce Blue Noise Plots where random jitter along the non-encoding plot dimension is replaced by optimizing all dots to keep a minimum distance in 2D i. e., Blue Noise. We evaluate the effectiveness as well as the aesthetics of Blue Noise Plots through both, a quantitative and a qualitative user study. The Python implementation of Blue Noise Plots is available here.Item High-Performance Graphics 2021 – Symposium Papers: Frontmatter(Eurographics Association, 2021) Binder, Nikolaus; Ritschel, Tobias; Binder, Nikolaus and Ritschel, TobiasItem Neural BRDF Representation and Importance Sampling(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Sztrajman, Alejandro; Rainer, Gilles; Ritschel, Tobias; Weyrich, Tim; Benes, Bedrich and Hauser, HelwigControlled capture of real‐world material appearance yields tabulated sets of highly realistic reflectance data. In practice, however, its high memory footprint requires compressing into a representation that can be used efficiently in rendering while remaining faithful to the original. Previous works in appearance encoding often prioritized one of these requirements at the expense of the other, by either applying high‐fidelity array compression strategies not suited for efficient queries during rendering, or by fitting a compact analytic model that lacks expressiveness. We present a compact neural network‐based representation of BRDF data that combines high‐accuracy reconstruction with efficient practical rendering via built‐in interpolation of reflectance. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling, critical for the accurate reconstruction of specular highlights. Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple real‐world datasets, and importance sampling performance for isotropic BRDFs mapped to two different analytic models.Item Neural Precomputed Radiance Transfer(The Eurographics Association and John Wiley & Sons Ltd., 2022) Rainer, Gilles; Bousseau, Adrien; Ritschel, Tobias; Drettakis, George; Chaine, Raphaëlle; Kim, Min H.Recent advances in neural rendering indicate immense promise for architectures that learn light transport, allowing efficient rendering of global illumination effects once such methods are trained. The training phase of these methods can be seen as a form of pre-computation, which has a long standing history in Computer Graphics. In particular, Pre-computed Radiance Transfer (PRT) achieves real-time rendering by freezing some variables of the scene (geometry, materials) and encoding the distribution of others, allowing interactive rendering at runtime. We adopt the same configuration as PRT - global illumination of static scenes under dynamic environment lighting - and investigate different neural network architectures, inspired by the design principles and theoretical analysis of PRT. We introduce four different architectures, and show that those based on knowledge of light transport models and PRT-inspired principles improve the quality of global illumination predictions at equal training time and network size, without the need for high-end ray-tracing hardware.Item Rendering 2023 CGF 42-4: Frontmatter(The Eurographics Association and John Wiley & Sons Ltd., 2023) Ritschel, Tobias; Weidlich, Andrea; Ritschel, Tobias; Weidlich, AndreaItem EUROGRAPHICS 2020: Posters Frontmatter(Eurographics Association, 2020) Ritschel, Tobias; Eilertsen, Gabriel; Ritschel, Tobias and Eilertsen, GabrielItem Pacific Graphics 2024 - Conference Papers and Posters: Frontmatter(The Eurographics Association, 2024) Chen, Renjie; Ritschel, Tobias; Whiting, Emily; Chen, Renjie; Ritschel, Tobias; Whiting, EmilyItem High Performance Graphics 2021 CGF 40-8: Frontmatter(The Eurographics Association and John Wiley & Sons Ltd., 2021) Binder, Nikolaus; Ritschel, Tobias; Binder, Nikolaus and Ritschel, Tobias