Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
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
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
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
A novel optical tracking algorithm for point-based projective invariant marker patterns
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
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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. There are four advantages to our method. First, the correspondence problem is solved entirely in 2D and therefore no stereo correspondence is needed. Consequently, we can use any number of cameras, including a single camera. Secondly, as opposed to stereo methods, we do not need to detect the same model point in two different cameras, and therefore our method is much more robust against occlusion. Thirdly, the subgraph matching algorithm can still detect a match even when parts of the graph are occluded, for example by the users hands. This also provides more robustness against occlusion. Finally, the error made in the pose estimation is significantly reduced as the amount of cameras is increased.