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International Symposium on Visual Computing

ISVC 2016: Advances in Visual Computing pp 449–461Cite as

Spatiotemporal LOD-Blending for Artifact Reduction in Multi-resolution Volume Rendering

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

Abstract

High-quality raycasting of multi-resolution volumetric datasets benefits from a well-informed working set selection that accounts for occlusions as well as output sensitivity. In this work, we suggest a feedback mechanism that provides a fine-grained level-of-detail selection for restricted working sets. To mitigate multi-resolution artifacts, our rendering solution combines spatial and temporal level-of-detail blending to provide smooth transitions between adjacent bricks of differing levels of detail and during working set adjustments. We also show how the sampling along rays needs to be adapted to produce a consistent result. Our implementation demonstrates that our spatiotemporal blending in combination with consistent sampling significantly reduces visual artifacts.

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Acknowledgments

We thank Rhadamés Carmona for early discussions of the spatiotemporal blending approach and Christopher Lux for providing the out-of-core volume rendering framework which we used to implement the techniques presented in this paper. This work was supported in part by the German Federal Ministry of Education and Research (BMBF) under grant 03IPT704X (project Big Data Analytics) and by the VRGeo Consortium. The seismic data set shown in this work is courtesy of Crown Minerals and the New Zealand Ministry of Economic Development (www.crownminerals.govt.nz).

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Authors and Affiliations

  1. Bauhaus-Universität Weimar, Weimar, Germany

    Sebastian Thiele, Carl-Feofan Matthes & Bernd Froehlich

Authors
  1. Sebastian Thiele
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  2. Carl-Feofan Matthes
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  3. Bernd Froehlich
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Corresponding author

Correspondence to Carl-Feofan Matthes .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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© 2016 Springer International Publishing AG

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Thiele, S., Matthes, CF., Froehlich, B. (2016). Spatiotemporal LOD-Blending for Artifact Reduction in Multi-resolution Volume Rendering. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_41

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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