Adequate reconstruction of transparent objects on a shoestring budget

Sai Kit Yeung*, Tai Pang Wu, Chi Keung Tang, Tony F. Chan, Stanley Osher

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

Reconstructing transparent objects is a challenging problem. While producing reasonable results for quite complex objects, existing approaches require custom calibration or somewhat expensive labor to achieve high precision. On the other hand, when an overall shape preserving salient and fine details is sufficient, we show in this paper a significant step toward solving the problem on a shoestring budget, by using only a video camera, a moving spotlight, and a small chrome sphere. Specifically, the problem we address is to estimate the normal map of the exterior surface of a given solid transparent object, from which the surface depth can be integrated. Our technical contribution lies in relating this normal reconstruction problem to one of graph-cut segmentation. Unlike conventional formulations, however, our graph is dual-layered, since we can see a transparent object's foreground as well as the background behind it. Quantitative and qualitative evaluation are performed to verify the efficacy of this practical solution.

Original languageEnglish (US)
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages2513-2520
Number of pages8
ISBN (Print)9781457703942
DOIs
StatePublished - Sep 23 2011
Externally publishedYes
Event2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 - Colorado Springs, CO, United States
Duration: Jun 20 2011Jun 25 2011

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
CountryUnited States
CityColorado Springs, CO
Period06/20/1106/25/11

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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