Perceptual organization and the representation of natural form
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
3D Symmetry Detection Using The Extended Gaussian Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering 3D Motion of Multiple Objects Using Adaptive Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
A Spherical Representation for Recognition of Free-Form Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Objects by Matching Oriented Points
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Free-Form Surface Registration Using Surface Signatures
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recognition of 3-D objects using the extended Gaussian image
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Faithful recovering of quadric surfaces from 3D range data
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Automatic crude patch registration: toward automatic 3D model building
Computer Vision and Image Understanding - Registration and fusion of range images
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Most 3D recording methods generate multiple partial reconstructions that must be integrated to form a complete model. The coarse registration step roughly aligns the parts with each other. Several methods for coarse registration have been developed that are based on matching points between different parts. These methods look for interest points and use a point signature that encodes the local surface geometry to find corresponding points. We developed a technique that is complementary to these methods. Local descriptions can fail or can be highly inefficient when the surfaces contain local symmetries. In stead of discarding these regions, we introduce a method that first uses the Gaussian image to detect planar, cylindrical and conical regions and uses this information to compute the rigid motion between the patches. For combining the information from multiple regions to a single solution, we use a a Hough space that accumulates votes for candidate transformations. Due to their symmetry, they update a subspace of parameter space in stead of a single bin. Experiments on real range data from different views of the same object show that the method can find the rigid motion to put the patches in the same coordinates system.