Using graph distance in object recognition
CSC '90 Proceedings of the 1990 ACM annual conference on Cooperation
Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Optical Tracking Using Projective Invariant Marker Pattern Properties
VR '03 Proceedings of the IEEE Virtual Reality 2003
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Estimation for Multiple Camera Systems
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
ARTag, a Fiducial Marker System Using Digital Techniques
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Improved Topological Fiducial Tracking in the reacTIVision System
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Optical tracking using line pencil fiducials
EGVE'04 Proceedings of the Tenth Eurographics conference on Virtual Environments
Optical tracking and calibration of tangible interaction devices
EGVE'05 Proceedings of the 11th Eurographics conference on Virtual Environments
Technical Section: A simulator-based approach to evaluating optical trackers
Computers and Graphics
A framework for performance evaluation of model-based optical trackers
EGVE'08 Proceedings of the 14th Eurographics conference on Virtual Environments
Hi-index | 0.00 |
In this paper, we describe a new optical tracking algorithm for pose estimation of interaction devices in virtual and augmented reality. Given a 3D model of the interaction device and a number of camera images, the primary difficulty in pose reconstruction is to find the correspondence between 2D image points and 3D model points. Most previous methods solved this problem by the use of stereo correspondence. Once the correspondence problem has been solved, the pose can be estimated by determining the transformation between the 3D point cloud and the model. Our approach is based on the projective invariant topology of graph structures. The topology of a graph structure does not change under projection: in this way we solve the point correspondence problem by a subgraph matching algorithm between the detected 2D image graph and the model graph. In addition to the graph tracking algorithm, we describe a number of related topics. These include a discussion on the counting of topologically different graphs, a theoretical error analysis, and a method for automatically estimating a device model. Finally, we show and discuss experimental results for the position and orientation accuracy of the tracker.