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BEGIN:VEVENT
DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (1)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T111300
DTEND;TZID=Asia/Tokyo:20241205T112700
UID:siggraphasia_SIGGRAPH Asia 2024_sess127_papers_398@linklings.com
SUMMARY:Differentiable Modeling of Material Spreading in Inkjet Printing f
 or Appearance Prediction
DESCRIPTION:Technical Papers\n\nEmiliano Luci (Max Planck Institute for In
 formatics), Fabio Pellacini (Università degli Studi di Modena e Reggio Emi
 lia), and Vahid Babaei (Max Planck Institute for Informatics)\n\nInkjet 3D
  printers produce solid shapes using very small voxels made of polymeric m
 aterials. While state-of-the-art methods for predicting the appearance of 
 inkjet-printed objects assume a perfect grid, the printed patterns have an
  irregular material distribution due to complex spreading behavior. This i
 rregularity leads to imprecise appearance predictive tools. Here, we\nprop
 ose a fully differentiable method that models the material spreading behav
 ior of inkjet printing. We use a differentiable simulator along with a dif
 ferentiable volume renderer. Then, using an image of only one printed cali
 bration pattern, we obtain a generalizable material spreading transformati
 on that can be applied to an input, nominal grid of materials and produce 
 the effective material grid. By taking into account the dynamics of the pr
 inting process, our method significantly outperforms state of the art 3D p
 rinting appearance prediction models.\n\nRegistration Category: Full Acces
 s, Full Access Supporter\n\nLanguage Format: English Language\n\nSession C
 hair: Lin Lu (Shandong University)
URL:https://asia.siggraph.org/2024/program/?id=papers_398&sess=sess127
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