Curve reconstruction from unorganized points
Computer Aided Geometric Design
A real-time tracker for markerless augmented reality
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Fully Automated and Stable Registration for Augmented Reality Applications
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Robust Visual Tracking for Non-Instrumented Augmented Reality
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Adaptive Line Tracking with Multiple Hypotheses for Augmented Reality
ISMAR '05 Proceedings of the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality
Monocular model-based 3D tracking of rigid objects
Foundations and Trends® in Computer Graphics and Vision
A mobile markerless AR system for maintenance and repair
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Fusion of 3d and appearance models for fast object detection and pose estimation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Characterization of contour regularities based on the Levenshtein edit distance
Pattern Recognition Letters
An improved fast edit approach for two-string approximated mean computation applied to OCR
Pattern Recognition Letters
Junction assisted 3D pose retrieval of untextured 3D models in monocular images
Computer Vision and Image Understanding
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We present a solution for automatic extraction of contour models out of polygonal CAD models. Such contour models can be used for markerless initialization and tracking purposes. To create a contour model we synthesize different views of the object using its CAD model. During the view generation we alternate the camera pose as well as light conditions in order to extract the most stable contours in terms of illumination invariance and view independence. We project the extracted 2D edges back into the 3D space and accumulate for every 3D point statistics over different views describing its visibility and stability under different illumination conditions. After filtering out all 3D points with probability below a certain threshold value we use the Euclidean Minimum Spanning Tree algorithm to get the connected contours out of the 3D point cloud. The result is a B-Spline or VRML representation of the most stable contours.