Implicit 3D modeling and tracking for anywhere augmentation

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Implicit 3D modeling and tracking for anywhere augmentation

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Virtual Reality Software and Technology archive
Proceedings of the 2007 ACM symposium on Virtual reality software and technology table of contents

Newport Beach, California

SESSION: Tracking, calibration & VR support table of contents

Pages: 19 - 28

Year of Publication: 2007

ISBN:978-1-59593-863-3

Authors

Sehwan Kim	University of California, Santa Barbara, CA
Stephen DiVerdi	University of California, Santa Barbara, CA
Jae Sik Chang	University of California, Santa Barbara, CA
Taehyuk Kang	University of California, Santa Barbara, CA
Ronald Iltis	University of California, Santa Barbara, CA
Tobias Höllerer	University of California, Santa Barbara, CA

Sponsors

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 26, Downloads (12 Months): 258, Citation Count: 0

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ABSTRACT

This paper presents an online 3D modeling and tracking methodology that uses aerial photographs for mobile augmented reality. Instead of relying on models which are created in advance, the system generates a 3D model for a real building on the fly by combining frontal and aerial views with the help of an optical sensor, an inertial sensor, a GPS unit and a few mouse clicks. A user's initial pose is estimated using an aerial photograph, which is retrieved from a database according to the user's GPS coordinates, and an inertial sensor which measures pitch. To track the user's position and orientation in real-time, feature-based tracking is carried out based on salient points on the edges and the sides of a building the user is keeping in view. We implemented camera pose estimators using both a least squares and an unscented Kalman filter (UKF) approach. The UKF approach results in more stable and reliable vision-based tracking. We evaluate the speed and accuracy of both approaches, and we demonstrate the usefulness of our computations as important building blocks for an Anywhere Augmentation scenario.

REFERENCES

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