Imperial College London
Alternate

ProfessorAbhijeetGhosh

Faculty of Engineering, Department of Computing

Professor of Graphics and Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8351abhijeet.ghosh Website

 
 
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Location

 

376Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Kim:2017:10.1109/CVPR.2017.162,
author = {Kim, J and Reshetouski, I and Ghosh, A},
doi = {10.1109/CVPR.2017.162},
pages = {1484--1492},
publisher = {IEEE},
title = {Acquiring axially-symmetric transparent objects using single-view transmission imaging},
url = {http://dx.doi.org/10.1109/CVPR.2017.162},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We propose a novel, practical solution for high quality reconstruction of axially-symmetric transparent objects. While a special case, such transparent objects are ubiquitous in the real world. Common examples of these are glasses, goblets, tumblers, carafes, etc., that can have very unique and visually appealing forms making their reconstruction interesting for vision and graphics applications. Our acquisition setup involves imaging such objects from a single viewpoint while illuminating them from directly behind with a few patterns emitted by an LCD panel. Our reconstruction step is then based on optimization of the objects geometry and its refractive index to minimize the difference between observed and simulated transmission/refraction of rays passing through the object. We exploit the objects axial symmetry as a strong shape prior which allows us to achieve robust reconstruction from a single viewpoint using a simple, commodity acquisition setup. We demonstrate high quality reconstruction of several common rotationally symmetric as well as more complex n-fold symmetric transparent objects with our approach.
AU  - Kim,J
AU  - Reshetouski,I
AU  - Ghosh,A
DO  - 10.1109/CVPR.2017.162
EP  - 1492
PB  - IEEE
PY  - 2017///
SN  - 1063-6919
SP  - 1484
TI  - Acquiring axially-symmetric transparent objects using single-view transmission imaging
UR  - http://dx.doi.org/10.1109/CVPR.2017.162
UR  - http://hdl.handle.net/10044/1/45709
ER  -
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