Accelerating Template-Based Matching on the GPU for AR Applications

Authors:
Yannick Allusse;Raphael Grasset;Mark Billinghurst
Affiliations:
HIT Lab NZ, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. e-mail: Yannick.Allusse@hitlabnz.org;HIT Lab NZ, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. e-mail: Raphael.Grasset@hitlabnz.org;HIT Lab NZ, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. e-mail: Mark.Billinghurst@hitlabnz.org
Venue:
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Year:
2007

Citing 2
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Abstract

Recently researchers have shown that it is possible to use GPU hardware for image processing and computer vision algorithms. We have been exploring how to use GPU hardware to improve marker-based tracking for AR Applications. In this paper we describe our findings and explored issues in the context of a standard fiducial tracking pipeline. We demonstrate the implementation of a template matching process on the GPU and the performance improvement gained in comparison to a traditional CPU implementation.