Robust Visual Tracking for Non-Instrumented Augmented Reality
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Graphical models for visual object recognition and tracking
Graphical models for visual object recognition and tracking
A Probabilistic Framework for 3D Visual Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Continuous surface-point distributions for 3D object pose estimation and recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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We present a novel way of performing pose estimation of known objects in 2D images. We follow a probabilistic approach for modeling objects and representing the observations. These object models are suited to various types of observable visual features, and are demonstrated here with edge segments. Even imperfect models, learned from single stereo views of objects, can be used to infer the maximum-likelihood pose of the object in a novel scene, using a Metropolis-Hastings MCMC algorithm, given a single, calibrated 2D view of the scene. The probabilistic approach does not require explicit model-to-scene correspondences, allowing the system to handle objects without individually-identifiable features. We demonstrate the suitability of these object models to pose estimation in 2D images through qualitative and quantitative evaluations, as we show that the pose of textureless objects can be recovered in scenes with clutter and occlusion.