Object pose from 2-D to 3-D point and line correspondences
International Journal of Computer Vision
Junctions: Detection, Classification, and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D object recognition and pose with relational indexing
Computer Vision and Image Understanding
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
International Journal of Computer Vision
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
EPnP: An Accurate O(n) Solution to the PnP Problem
International Journal of Computer Vision
Automatic contour model creation out of polygonal CAD models for markerless Augmented Reality
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
CAD-based recognition of 3D objects in monocular images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Polygon crawling: feature edge extraction from a general polygonal surface for mesh generation
Engineering with Computers - Special Issue: 14th International Meshing Roundtable in 2005. Guest Editor: Byron W. Hanks
Object recognition using junctions
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Two Bayesian methods for junction classification
IEEE Transactions on Image Processing
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This article presents a comprehensive framework for the recognition of untextured 3D models in a single image. The method proposed here is capable of recovering a 3D pose in a few hundred of milliseconds, which is a difficult challenge using this type of model. This proposal deals with 3D models that lack texture, so geometry features of the model are used as a basis of the 3D pose retrieval. An automatic process extracts the junctions and contours of the model, replacing the user interaction. Junctions will provide us an efficient mechanism to generate candidate matches, while contours will select the correct match based on a robust shape similarity evaluation. Our method only requires the 3D triangle mesh of the model as input, since the rest of the process is done automatically. We demonstrate the behaviour of our approach against a variety of real scenes and models. Moreover, we explain how to face the first pose problem in a robust way using a history of votes. We also present a study of the method parameterisation, describing the influence of each parameter.