Using Extremal Boundaries for 3-D Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
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
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Object recognition in cluttered environments by segment-based stereo vision
International Journal of Computer Vision
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curve and Surface Duals and the Recognition of Curved 3D Objects from their Silhouettes
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
A Fast Initialization Method for Edge-based Registration Using an Inclination Constraint
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
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This paper describes a method for model-based 3D object localization The object model consists of a triangular surface mesh, model points, and model geometrical features Model points and model geometrical features are generated using contour generators, which are estimated by the occluding contours of projected images of the triangular surface mesh from multiple viewing directions, and they are maintained depending on the viewing direction Multiple hypotheses for approximate model position and orientation are generated by comparing model geometrical features and data geometrical features The multiple hypotheses are limited by using the viewing directions that are used to generate model geometrical features Each hypothesis is verified and improved by using model points and 3D boundaries, which are reconstructed by segment-based stereo vision In addition, each hypothesis is improved by using the triangular surface mesh and 3D boundaries Experimental results show the effectiveness of the proposed method.