Surface reconstruction from unorganized points with l0 gradient minimization

Huibin Li, Yibao Li, Ruixuan Yu, Jian Sun, Junseok Kim

Research output: Contribution to journalArticle

Abstract

To reconstruct surface from unorganized points in three-dimensional Euclidean space, we propose a novel efficient and fast method by using l0 gradient minimization, which can directly measure the sparsity of a solution and produce sharper surfaces. Therefore, the proposed method is particularly effective for sharpening major edges and removing noise. Unlike the Poisson surface reconstruction approach and its extensions, our method does not depend on the accurate directions of normal vectors of the unorganized points. The resulting algorithm is developed using a half-quadratic splitting method and is based on decoupled iterations that are alternating over a smoothing step realized by a Poisson approach and an edge-preserving step through an optimization formulation. This iterative algorithm is easy to implement. Various tests are presented to demonstrate that our method is robust to point noise, normal noise and data holes, and thus produces good surface reconstruction results.

Original languageEnglish
Pages (from-to)108-118
Number of pages11
JournalComputer Vision and Image Understanding
Volume169
DOIs
Publication statusPublished - 2018 Apr 1

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Surface reconstruction

Keywords

  • fast Fourier transform
  • l gradient minimization
  • l sparsity
  • Point cloud
  • Surface reconstruction

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition

Cite this

Surface reconstruction from unorganized points with l0 gradient minimization. / Li, Huibin; Li, Yibao; Yu, Ruixuan; Sun, Jian; Kim, Junseok.

In: Computer Vision and Image Understanding, Vol. 169, 01.04.2018, p. 108-118.

Research output: Contribution to journalArticle

Li, Huibin ; Li, Yibao ; Yu, Ruixuan ; Sun, Jian ; Kim, Junseok. / Surface reconstruction from unorganized points with l0 gradient minimization. In: Computer Vision and Image Understanding. 2018 ; Vol. 169. pp. 108-118.
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