Texture overlay onto non-rigid surface using commodity depth camera

Tomoki Hayashi, Francois De Sorbier, Hideo Saito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

We present a method for overlaying a texture onto a non-rigid surface using a commodity depth camera. The depth cameras are able to capture 3-D data of a surface in real-time, and have several advantages compared with methods using only standard color cameras. However, it is not easy to register a 3-D deformable mesh to a point cloud of the non-rigid surface while keeping its geometrical topology. In order to solve this problem, our method starts by learning many representative meshes to generate surface deformation models. Then, while capturing 3-D data, we register a feasible 3-D mesh to the target surface and overlay a template texture onto the registered mesh. Even if the depth data are noisy or sparse, the learning-based method provides us with a smooth surface mesh. In addition, our method can be applied to real-time applications. In our experiments, we show some augmented reality results of texture overlay onto a non-textured T-shirt.

Original languageEnglish
Title of host publicationVISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications
Pages66-71
Number of pages6
Publication statusPublished - 2012 Jun 15
EventInternational Conference on Computer Vision Theory and Applications, VISAPP 2012 - Rome, Italy
Duration: 2012 Feb 242012 Feb 26

Publication series

NameVISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications
Volume2

Other

OtherInternational Conference on Computer Vision Theory and Applications, VISAPP 2012
Country/TerritoryItaly
CityRome
Period12/2/2412/2/26

Keywords

  • Augmented reality
  • Deformable 3-D registration
  • Depth camera
  • Principal component analysis

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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