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Author:

Cai, Peng (Cai, Peng.) | Kong, Dehui (Kong, Dehui.) (Scholars:孔德慧) | Wang, Shaofan (Wang, Shaofan.) | Yin, Baocai (Yin, Baocai.) (Scholars:尹宝才) | Huo, Yi (Huo, Yi.)

Indexed by:

EI Scopus

Abstract:

Previous ray tracing methods usually treat point-cloud models with attributes including coordinates, normals and radius of points. While 3D coordinates of points can be precisely acquired by equipments, normals and radius of points need to be computed before ray tracing algorithm. Such computation always takes a long time, and produces various errors. This paper proposes a novel ray tracing method of point-cloud models, based on K-nearest-neighbors of iterative points. The method locates a finite number of nearest points to the iterative point in a ray, and computes the normal vector of local surface by using area-weighted average of normals of triangles, which consist of the iterative point and the Knearest points. The intersection and its normal are obtained by firstly computing intersections between ray and regular triangles, and then blending normals of regular sampling points of local surface, which produce smooth rendering effect and represent more geometric details. Numerical experiments show that our method ensures coherence of normals of intersections, especially for half-open or fragmented point clouds. Moreover, our method can progressively render point clouds in multi-resolution mode. We employ a balanced binary tree to locate the nearest points, and employ grids to avoid unnecessarily iterative computation in the space of point clouds. ©, 2015, Journal of Information and Computational Science. All right reserved.

Keyword:

Blending Numerical methods Ray tracing Binary trees Cloud computing Nearest neighbor search Iterative methods Rendering (computer graphics)

Author Community:

  • [ 1 ] [Cai, Peng]Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
  • [ 2 ] [Kong, Dehui]Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
  • [ 3 ] [Wang, Shaofan]Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
  • [ 4 ] [Yin, Baocai]Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
  • [ 5 ] [Huo, Yi]Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China

Reprint Author's Address:

  • 孔德慧

    [kong, dehui]beijing key laboratory of multimedia and intelligent software technology, college of metropolitan transportation, beijing university of technology, beijing, china

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Source :

Journal of Information and Computational Science

ISSN: 1548-7741

Year: 2015

Issue: 13

Volume: 12

Page: 4929-4941

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 11

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