Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
A computational approach to edge detection
Readings in computer vision: issues, problems, principles, and paradigms
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Towards a General Multi-View Registration Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Curvature and Tangent Direction by Median Filtered Differencing
ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking
International Journal of Computer Vision
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
CAD-based recognition of 3D objects in monocular images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Multiview registration for large data sets
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Pose Estimation of Known Objects by Efficient Silhouette Matching
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Using specular highlights as pose invariant features for 2D-3D pose estimation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We propose a new method for refining 6-DOF pose of rigid transparent objects. The algorithm is based on minimizing the distance between edges in a test image and a set of edges produced by the training model with a specific pose. The model is scanned with a monocular camera and a 3D sensor such as a Kinect device. The pose is estimated from a monocular image or a stereo pair. The method does not require a CAD model of the object. We demonstrate experimental results on a set of kitchen items essential for any home and office environment.