research-article

Tomographic surface reconstruction from point cloud

Authors:

Hiromasa SuzukiAuthors Info & Claims

Computers and Graphics, Volume 46, Issue C

Pages 55 - 63

https://doi.org/10.1016/j.cag.2014.09.034

Published: 01 February 2015 Publication History

Abstract

Inspired by computed tomography (CT), this paper presents a novel surface reconstruction algorithm, tomographic surface reconstruction, to reconstruct a surface mesh from a point cloud equipped with oriented normals. In the process of scanning a real object using an X-ray CT system, it generates a sinogram consisting of projection images that are maps of X-ray transmission lengths, and then, a tomogram (CT volume) is reconstructed from the sinogram. A hole-free surface mesh is then easily obtained by polygonizing an isosurface. To adopt this CT paradigm to surface reconstruction from a point cloud, only a scheme to generate a sinogram from a point cloud is required. The value of a sinogram for surface reconstruction can be defined as the sum of the distances between the intersecting points of a ray and the underlying surface, which are defined as the maxima of the point density. While ordinary CT scanning uses projection directions which share a single rotation axis, tomographic surface reconstruction adopts randomly selected projection directions and successfully improved the reconstruction robustness. By applying an iterative CT reconstruction to the sinogram, the algorithm generates a tomogram whose boundary between the foreground and background approximates the surface of the object. The effectiveness for a point cloud with a lack of sampling and outliers is demonstrated from experimental results. Graphical abstractDisplay Omitted HighlightsA novel approach for surface reconstruction using a CT-reconstruction algorithm.Enhancing robustness to compute transmission-length for a point cloud.Robustness over noise and outliers is demonstrated.

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Cited By

Zhou QDaricka MMa YHuang M(2022)Data Reconstruction and Optimal Design of Auto Rearview Mirror Based on REProceedings of the 2022 3rd International Conference on Artificial Intelligence in Electronics Engineering10.1145/3512826.3512835(92-96)Online publication date: 11-Jan-2022
https://dl.acm.org/doi/10.1145/3512826.3512835
Berger MTagliasacchi ASeversky LAlliez PGuennebaud GLevine JSharf ASilva C(2017)A Survey of Surface Reconstruction from Point CloudsComputer Graphics Forum10.1111/cgf.1280236:1(301-329)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1111/cgf.12802

Tomographic surface reconstruction from point cloud
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics
2. Networks
  1. Network protocols

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cover image Computers and Graphics

Computers and Graphics Volume 46, Issue C

February 2015

364 pages

ISSN:0097-8493

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Copyright © Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2015

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Cited By

Zhou QDaricka MMa YHuang M(2022)Data Reconstruction and Optimal Design of Auto Rearview Mirror Based on REProceedings of the 2022 3rd International Conference on Artificial Intelligence in Electronics Engineering10.1145/3512826.3512835(92-96)Online publication date: 11-Jan-2022
https://dl.acm.org/doi/10.1145/3512826.3512835
Berger MTagliasacchi ASeversky LAlliez PGuennebaud GLevine JSharf ASilva C(2017)A Survey of Surface Reconstruction from Point CloudsComputer Graphics Forum10.1111/cgf.1280236:1(301-329)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1111/cgf.12802

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