40-Issue 2

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https://diglib.eg.org/handle/10.2312/2633016

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Browsing 40-Issue 2 by Subject "Applied computing"

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    Higher Dimensional Graphics: Conceiving Worlds in Four Spatial Dimensions and Beyond
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Cavallo, Marco; Mitra, Niloy and Viola, Ivan
    While the interpretation of high-dimensional datasets has become a necessity in most industries, the spatial visualization of higher-dimensional geometry has mostly remained a niche research topic for mathematicians and physicists. Intermittent contributions to this field date back more than a century, and have had a non-negligible influence on contemporary art and philosophy. However, most contributions have focused on the understanding of specific mathematical shapes, with few concrete applications. In this work, we attempt to revive the community's interest in visualizing higher dimensional geometry by shifting the focus from the visualization of abstract shapes to the design of a broader hyper-universe concept, wherein 3D and 4D objects can coexist and interact with each other. Specifically, we discuss the content definition, authoring patterns, and technical implementations associated with the process of extending standard 3D applications as to support 4D mechanics. We operationalize our ideas through the introduction of a new hybrid 3D/4D videogame called Across Dimensions, which we developed in Unity3D through the integration of our own 4D plugin.
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    Neural Acceleration of Scattering-Aware Color 3D Printing
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Rittig, Tobias; Sumin, Denis; Babaei, Vahid; Didyk, Piotr; Voloboy, Alexey; Wilkie, Alexander; Bickel, Bernd; Myszkowski, Karol; Weyrich, Tim; Krivánek, Jaroslav; Mitra, Niloy and Viola, Ivan
    With the wider availability of full-color 3D printers, color-accurate 3D-print preparation has received increased attention. A key challenge lies in the inherent translucency of commonly used print materials that blurs out details of the color texture. Previous work tries to compensate for these scattering effects through strategic assignment of colored primary materials to printer voxels. To date, the highest-quality approach uses iterative optimization that relies on computationally expensive Monte Carlo light transport simulation to predict the surface appearance from subsurface scattering within a given print material distribution; that optimization, however, takes in the order of days on a single machine. In our work, we dramatically speed up the process by replacing the light transport simulation with a data-driven approach. Leveraging a deep neural network to predict the scattering within a highly heterogeneous medium, our method performs around two orders of magnitude faster than Monte Carlo rendering while yielding optimization results of similar quality level. The network is based on an established method from atmospheric cloud rendering, adapted to our domain and extended by a physically motivated weight sharing scheme that substantially reduces the network size. We analyze its performance in an end-to-end print preparation pipeline and compare quality and runtime to alternative approaches, and demonstrate its generalization to unseen geometry and material values. This for the first time enables full heterogenous material optimization for 3D-print preparation within time frames in the order of the actual printing time.