Computer vision, models and inspection
Computer vision, models and inspection
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
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Rigid and affine registration of smooth surfaces using differential properties
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Segmentation of range images as the search for geometric parametric models
International Journal of Computer Vision
Simultaneous registration of multiple range views for use in reverse engineering of CAD models
Computer Vision and Image Understanding - Special issue on CAD-based computer vision
Registering Multiview Range Data to Create 3D Computer Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Faithful Least-Squares Fitting of Spheres, Cylinders, Cones and Tori for Reliable Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Registration and Integration of Multiple Range Images for 3-D Model Construction
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Extension of the ICP Algorithm to Non-Rigid Intensity-Based Registration of 3D Volumes
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Transformation image into graphics
Integrated image and graphics technologies
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We present a new method for registration of range images, which is based on the results we obtain from the segmentation process. We need two range images segmented into regions, each of them modeled by a parametric model and the approximation of the transformation between the two range images. Then two sets of corresponding points, one from each range image, are chosen and the transformation between them is computed to further refine the initial approximation of the transformation. The novelty is how we obtain the a corresponding points for the original sets of points from the range image. Namely, to obtain them we project set of points from the first range image onto geometric parametric models taht were recovered in the second range image and viceversa. This way we obtain two sets of corresponding points. Then we compute the transformation between the two sets. Few iterations are required to improve the initial approximation of the transformation. The results have shown a significant improvement in precision of the registration in comparison with traditional approches.