The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
Structural Indexing: Efficient 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Simple Algorithm for Nearest Neighbor Search in High Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of free-form object representation and recognition techniques
Computer Vision and Image Understanding
Object recognition: fundamentals and case studies
Object recognition: fundamentals and case studies
Computer and Robot Vision
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
The Complex EGI: A New Representation for 3-D Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Free-Form Surface Registration Using Surface Signatures
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Box-like Superquadric Recovery in Range Images by Fusing Region and Boundary Information
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Large data sets and confusing scenes in 3-D surface matching and recognition
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
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In this paper we propose a new technique for modeling three-dimensional rigid objects by encoding the fluctuation of the surface and the variation of its normal around an oriented surface point, as the surface expands. The surface of the object is encoded into three vectors as the surface signature on each point, and then the collection of signatures is used to model and match the object. The signatures encode the curvature, symmetry, and convexity of the surface around an oriented point. This modeling technique is robust to scale, orientation, sampling resolution, noise, occlusion, and cluttering.