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dc.contributor.authorRittig, Tobiasen_US
dc.contributor.authorSumin, Denisen_US
dc.contributor.authorBabaei, Vahiden_US
dc.contributor.authorDidyk, Piotren_US
dc.contributor.authorVoloboy, Alexeyen_US
dc.contributor.authorWilkie, Alexanderen_US
dc.contributor.authorBickel, Bernden_US
dc.contributor.authorMyszkowski, Karolen_US
dc.contributor.authorWeyrich, Timen_US
dc.contributor.authorKrivánek, Jaroslaven_US
dc.contributor.editorMitra, Niloy and Viola, Ivanen_US
dc.date.accessioned2021-04-09T08:00:16Z
dc.date.available2021-04-09T08:00:16Z
dc.date.issued2021
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.142626
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf142626
dc.description.abstractWith 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectReflectance modeling
dc.subjectVolumetric models
dc.subjectApplied computing
dc.subjectComputer
dc.subjectaided manufacturing
dc.titleNeural Acceleration of Scattering-Aware Color 3D Printingen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersFabrication
dc.description.volume40
dc.description.number2
dc.identifier.doi10.1111/cgf.142626
dc.identifier.pages205-219


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