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Adaptive Illumination Sampling for Direct Volume Rendering

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Advances in Computer Graphics (CGI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12221))

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Abstract

Direct volume rendering is used to visualize data from sources such as tomographic imaging devices. The perception of certain structures depends very much on visual cues such as lighting and shadowing. According illumination techniques have been proposed for both surface rendering and volume rendering. However, in the case of direct volume rendering, some form of precomputation is typically required for real-time rendering. This however limits the application of the visualization. In this work we present adaptive volumetric illumination sampling, a ray-casting-based direct volume rendering method that strongly reduces the amount of necessary illumination computations without introducing any noise. By combining it with voxel cone tracing, realistic lighting including ambient occlusion and image-based lighting is facilitated in real-time. The method only requires minimal precomputation and allows for interactive transfer function updates and clipping of the visualized data.

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Notes

  1. 1.

    https://docs.microsoft.com/en-us/windows/mixed-reality/app-quality-criteria.

  2. 2.

    2048 \(\times \) 1080 pixel.

  3. 3.

    https://klacansky.com/open-scivis-datasets/.

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Acknowledgements

We thank our partners, especially Prof. Dr. Weyhe and his team at the Pius hospital Oldenburg in the department of general and visceral surgery, who provided the “Torso” dataset. This research has been funded by the German Federal Ministry of Education and Research (BMBF) in the project VIVATOP (funding code 16SV8078).

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Authors and Affiliations

  1. Fraunhofer MEVIS, Am Fallturm 1, 28359, Bremen, Germany

    Valentin Kraft, Florian Link, Andrea Schenk & Christian Schumann

Authors
  1. Valentin Kraft
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  2. Andrea Schenk
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  3. Christian Schumann
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Corresponding author

Correspondence to Valentin Kraft .

Editor information

Editors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Nadia Magnenat-Thalmann

  2. University of Crete, Heraklion, Greece

    Constantine Stephanidis

  3. University of Macau, Macau, China

    Enhua Wu

  4. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. University of Sydney, Sydney, Australia

    Jinman Kim

  7. University of Crete, Heraklion, Greece

    George Papagiannakis

  8. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

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Kraft, V., Link, F., Schenk, A., Schumann, C. (2020). Adaptive Illumination Sampling for Direct Volume Rendering. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2020. Lecture Notes in Computer Science(), vol 12221. Springer, Cham. https://doi.org/10.1007/978-3-030-61864-3_10

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  • DOI: https://doi.org/10.1007/978-3-030-61864-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61863-6

  • Online ISBN: 978-3-030-61864-3

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