Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Augmented Reality Camera Tracking with Homographies
IEEE Computer Graphics and Applications
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ECCV '92 Proceedings of the Second European Conference on Computer Vision
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ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
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ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
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
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Landmark Real-Time Recognition and Positioning for Pedestrian Navigation
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
3D real-time positioning for autonomous navigation using a nine-point landmark
Pattern Recognition
A new marker design for a robust marker tracking system against occlusions
Computer Animation and Virtual Worlds
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For three-dimensional video-based augmented reality applications, accurate measurements of the 6DOF camera pose relative to the real world are required for proper registration of the virtual objects. This paper presents an accurate and robust system for real-time 6DOF camera pose tracking based on natural features in an arbitrary scene. Crucially, the calculation is based on pre-captured reference images. This prevents a gradual increase in the camera position error. Point features in the current image frame are first matched to two spatially separated reference images. This wide baseline correspondence problem is overcome by constructing (1) a global homography between current and previous image frame and (2) local affine transforms derived from known matches between previous frame and reference images. Chaining these two mappings constrains the search for potential matches in the reference images and allows the warping of corner intensity neighborhoods so that a viewpoint invariant similarity measure for assessing potential point matches can be defined. We then minimize deviations from the two-view and three-view constraints between the reference images and current frame as a function of the camera motion parameters to obtain an estimate of the current camera pose relative to the reference images. This calculation is stabilized using a recursive form of temporal regularization similar in spirit to the Kalman filter. We can track camera pose reliably over hundreds of image frames and realistically integrate three-dimensional virtual objects with only slight jitter. This paper also tries to simplify the above described algorithm and present a real-time, robust tracking system based on computing homographies. Homography can exactly describe the image motion between two frames when the camera motion is pure rotation, or it is viewing a planar scene. For outdoor registration applications, the system is robust under small translations as long as the majority of the scene contents are distant.