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Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU

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Computer Animation and Social Agents (CASA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1300))

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Abstract

This paper presents a novel hybrid model comprising both surface mesh and the metaballs which occupy organs’ interior for the soft tissue modeling. Through the utility of metaballs, we are capable of simplifying the organ interior using a set of overlapping spheres with different radii. We first develop an adaptive approach based on Voronoi Diagram for the initialization of inner metaballs. Then, we resort to global optimization and devise an electrostatic attraction technique to drive the metaballs to best fill the space inside the organ’s boundary. We simplify the surgical instrument as a collection of cylinders with different radii and orientation, and develop an adaptive collision detection method to facilitate the collision between the surgical instrument and metaballs. Our framework is built on the parallel computation architecture of CUDA, and thus can afford interactive performance on a commodity desktop. To illustrate the effectiveness, the above techniques have all been integrated into a VR-based ventriculoscopic surgery simulator.

This research is supported in part by National Key R&D Program of China (No. 2017YFC0108104), China Postdoctoral Science Foundation (2019M660527), National Natural Science Foundation of China (NO. 61872020, 61977063, 61672149).

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Author information

Authors and Affiliations

  1. National Engineering Laboratory for Cyberlearning and Intelligent Technology, Beijing Normal University, Beijing, China

    Yang Shen

  2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Huiwei Feng, Jian Su & Junjun Pan

  3. Peng Cheng Lab, Shenzhen, China

    Junjun Pan

Authors
  1. Yang Shen
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  2. Huiwei Feng
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  3. Jian Su
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  4. Junjun Pan
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Corresponding author

Correspondence to Junjun Pan .

Editor information

Editors and Affiliations

  1. Faculty of Science and Technology, Bournemouth University, Poole, UK

    Feng Tian

  2. Faculty of Media and Communication, Bournemouth University, Poole, UK

    Xiaosong Yang

  3. École Polytechnique Fédérale de Lausa, Lausanne, Switzerland

    Daniel Thalmann

  4. Computer Science Department, Zhejiang University, Hangzhou, China

    Weiwei Xu

  5. Faculty of Media and Communication, Bournemouth University, Poole, UK

    Jian Jun Zhang

  6. MIRALab/C.U.I., University of Geneva, Geneva, Switzerland

    Nadia Magnenat Thalmann

  7. Faculty of Media and Communication, Bournemouth University, Poole, UK

    Jian Chang

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Shen, Y., Feng, H., Su, J., Pan, J. (2020). Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU. In: Tian, F., et al. Computer Animation and Social Agents. CASA 2020. Communications in Computer and Information Science, vol 1300. Springer, Cham. https://doi.org/10.1007/978-3-030-63426-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-63426-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63425-4

  • Online ISBN: 978-3-030-63426-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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