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

Yang Shen; Huiwei Feng; Jian Su; Junjun Pan

doi:10.1007/978-3-030-63426-1_5

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

Yang Shen
, Huiwei Feng
, Jian Su
, Junjun Pan^*

^*Corresponding author for this work

School of Computer Science and Engineering

Beijing Normal University
Beihang University
Peng Cheng Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Computer Animation and Social Agents - 33rd International Conference on Computer Animation and Social Agents, CASA 2020, Proceedings
Editors	Feng Tian, Xiaosong Yang, Daniel Thalmann, Weiwei Xu, Jian Jun Zhang, Nadia Magnenat Thalmann, Jian Chang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	43-50
Number of pages	8
ISBN (Print)	9783030634254
DOIs	https://doi.org/10.1007/978-3-030-63426-1_5
State	Published - 2020
Event	33rd International Conference on Computer Animation and Social Agents, CASA 2020 - Bournemouth, United Kingdom Duration: 13 Oct 2020 → 15 Oct 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1300
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	33rd International Conference on Computer Animation and Social Agents, CASA 2020
Country/Territory	United Kingdom
City	Bournemouth
Period	13/10/20 → 15/10/20

Keywords

Collision detection
GPU
Metaballs
Neurosurgery

Access to Document

10.1007/978-3-030-63426-1_5

Cite this

Shen, Y., Feng, H., Su, J., & Pan, J. (2020). Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU. In F. Tian, X. Yang, D. Thalmann, W. Xu, J. J. Zhang, N. M. Thalmann, & J. Chang (Eds.), Computer Animation and Social Agents - 33rd International Conference on Computer Animation and Social Agents, CASA 2020, Proceedings (pp. 43-50). (Communications in Computer and Information Science; Vol. 1300). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63426-1_5

Shen, Yang ; Feng, Huiwei ; Su, Jian et al. / Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU. Computer Animation and Social Agents - 33rd International Conference on Computer Animation and Social Agents, CASA 2020, Proceedings. editor / Feng Tian ; Xiaosong Yang ; Daniel Thalmann ; Weiwei Xu ; Jian Jun Zhang ; Nadia Magnenat Thalmann ; Jian Chang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 43-50 (Communications in Computer and Information Science).

@inproceedings{c01088c7cfd4430b8a67e29530cecff1,

title = "Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU",

abstract = "This paper presents a novel hybrid model comprising both surface mesh and the metaballs which occupy organs{\textquoteright} 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{\textquoteright}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.",

keywords = "Collision detection, GPU, Metaballs, Neurosurgery",

author = "Yang Shen and Huiwei Feng and Jian Su and Junjun Pan",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 33rd International Conference on Computer Animation and Social Agents, CASA 2020 ; Conference date: 13-10-2020 Through 15-10-2020",

year = "2020",

doi = "10.1007/978-3-030-63426-1\_5",

language = "英语",

isbn = "9783030634254",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "43--50",

editor = "Feng Tian and Xiaosong Yang and Daniel Thalmann and Weiwei Xu and Zhang, \{Jian Jun\} and Thalmann, \{Nadia Magnenat\} and Jian Chang",

booktitle = "Computer Animation and Social Agents - 33rd International Conference on Computer Animation and Social Agents, CASA 2020, Proceedings",

address = "德国",

}

Shen, Y, Feng, H, Su, J & Pan, J 2020, Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU. in F Tian, X Yang, D Thalmann, W Xu, JJ Zhang, NM Thalmann & J Chang (eds), Computer Animation and Social Agents - 33rd International Conference on Computer Animation and Social Agents, CASA 2020, Proceedings. Communications in Computer and Information Science, vol. 1300, Springer Science and Business Media Deutschland GmbH, pp. 43-50, 33rd International Conference on Computer Animation and Social Agents, CASA 2020, Bournemouth, United Kingdom, 13/10/20. https://doi.org/10.1007/978-3-030-63426-1_5

Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU. / Shen, Yang; Feng, Huiwei; Su, Jian et al.
Computer Animation and Social Agents - 33rd International Conference on Computer Animation and Social Agents, CASA 2020, Proceedings. ed. / Feng Tian; Xiaosong Yang; Daniel Thalmann; Weiwei Xu; Jian Jun Zhang; Nadia Magnenat Thalmann; Jian Chang. Springer Science and Business Media Deutschland GmbH, 2020. p. 43-50 (Communications in Computer and Information Science; Vol. 1300).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU

AU - Shen, Yang

AU - Feng, Huiwei

AU - Su, Jian

AU - Pan, Junjun

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Collision detection

KW - GPU

KW - Metaballs

KW - Neurosurgery

UR - https://www.scopus.com/pages/publications/85097653898

U2 - 10.1007/978-3-030-63426-1_5

DO - 10.1007/978-3-030-63426-1_5

M3 - 会议稿件

AN - SCOPUS:85097653898

SN - 9783030634254

T3 - Communications in Computer and Information Science

SP - 43

EP - 50

BT - Computer Animation and Social Agents - 33rd International Conference on Computer Animation and Social Agents, CASA 2020, Proceedings

A2 - Tian, Feng

A2 - Yang, Xiaosong

A2 - Thalmann, Daniel

A2 - Xu, Weiwei

A2 - Zhang, Jian Jun

A2 - Thalmann, Nadia Magnenat

A2 - Chang, Jian

PB - Springer Science and Business Media Deutschland GmbH

T2 - 33rd International Conference on Computer Animation and Social Agents, CASA 2020

Y2 - 13 October 2020 through 15 October 2020

ER -

Shen Y, Feng H, Su J, Pan J. Efficient Metaballs-Based Collision Detection for VR Neurosurgery Simulation on GPU. In Tian F, Yang X, Thalmann D, Xu W, Zhang JJ, Thalmann NM, Chang J, editors, Computer Animation and Social Agents - 33rd International Conference on Computer Animation and Social Agents, CASA 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 43-50. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-63426-1_5

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this