Introduction to non-linear optimization
Introduction to non-linear optimization
Robust regression and outlier detection
Robust regression and outlier detection
Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Uncertainty to Visual Exploration
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Recognizing geons from superquadratics fitted to range data
Image and Vision Computing - Special issue: range image understanding
Volumetric segmentation of range images of 3D objects using superquadric models
CVGIP: Image Understanding
Volumetric models in computer vision—an overview
Journal of Computing and Information Technology
Proceedings of the International NSF-ARPA Workshop on Object Representation in Computer Vision
Global and local deformations of solid primitives
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Experimental comparison of superquadric fitting objective functions
Pattern Recognition Letters
Genetic algorithms for 3D reconstruction with supershapes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Segmentation of juxtapleural pulmonary nodules using a robust surface estimate
Journal of Biomedical Imaging
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Undeformed superquadrics are volumetric modeling primitives with an extensive shape vocabulary that are described by only 5 parameters. Fitting these models viewpoint invariantly to range data enables classification based on the superquadric parameters. However, current recovery routines show several limitations, especially when the algorithms are applied to range images instead of true 3D images. In this paper problems with the common superquadric recovery procedure are identified and solutions are presented.