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Cited by 223D Shape Modeling and AnalysisFace recognition and analysisMedical Image Segmentation Techniques

Efficient groupwise non-rigid registration of textured surfaces

Sidorov, Kirill A., Richmond, Stephen, Marshall, David·2011·CVPR 2011
DOI 10.1109/cvpr.2011.5995632
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Abstract

Advances in 3D imaging have recently made 3D surface scanners, capable of capturing textured surfaces at video rate, affordable and common in computer vision. This is a relatively new source of data, the potential of which has not yet been fully exploited as the problem of non-rigid registration of surfaces is difficult. While registration based on shape alone has been an active research area for some time, the problem of registering surfaces based on texture information has not been addressed in a principled way. We propose a novel, efficient and reliable, fully automatic method for performing groupwise non-rigid registration of textured surfaces, such as those obtained with 3D scanners. We demonstrate the robustness of our approach on 3D scans of human faces, including the notoriously difficult case of inter-subject registration. We show how our method can be used to build high-quality 3D models of appearance fully automatically.

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Crossref · 10.1109/cvpr.2011.5995632

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • As has been found in medical imaging, registration utilizing heterogeneous information (e.g. geometry and texture [41], [120]) may give higher accuracy.

    “As has been found in medical imaging, registration utilizing heterogeneous information (e.g.geometry and texture [41], [120]) may give higher accuracy.”

    From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [120]

  • The minimum description length criterion is well-known for determining the best model from data; it has been used in image registration [119], face (texture and depth) registration [120] and medical data registration [121] in computer vision, but is relatively little used in computer graphics and shape analysis.

    “In information theory, the minimum description length criterion is well-known for determining the best model from data; it has been used in image registration [119], face (texture and depth) registration [120] and medical data registration [121] in computer vision, but is relatively little used in computer graphics and shape analysis.”

    From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [120]

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Related

Authors
Sidorov, K. A., Richmond, S., & Marshall, D.
Affiliations
Cardiff University (GB)
ORCID
Venue
CVPR 2011
Publisher
IEEE
Published
2011-06
Pages
2401-2408
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2011.5995632
OpenAlex
W2052504501
Cited by
22
References
20
Citations by year
2022: 2 · 2021: 1 · 2019: 1 · 2018: 5 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 3 · 2012: 7
FWCI
6.43
Type
conference paper
Language
en
Open access
closed
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
  • ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
groupwise registration, non-rigid registration, textured surfaces, 3D surface scanning, 3D face scans, inter-subject registration

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