Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Volumetric segmentation of range images of 3D objects using superquadric models
CVGIP: Image Understanding
Implicit reconstruction of solids from cloud point sets
SMA '95 Proceedings of the third ACM symposium on Solid modeling and applications
Segmentation and recovery of superquadrics: computational imaging and vision
Segmentation and recovery of superquadrics: computational imaging and vision
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Darboux Frames, Snakes, and Super-Quadrics: Geometry from the Bottom Up
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting Undeformed Superquadrics to Range Data: Improving Model Recovery and Classification
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Experimental comparison of superquadric fitting objective functions
Pattern Recognition Letters
Reliable Recovery of Piled Box-like Objects via Parabolically Deformable Superquadrics
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Superquadrics and Angle-Preserving Transformations
IEEE Computer Graphics and Applications
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Supershape model is a recent primitive that represents numerous 3D shapes with several symmetry axes. The main interest of this model is its capability to reconstruct more complex shape than superquadric model with only one implicit equation. In this paper we propose a genetic algorithms to reconstruct a point cloud using those primitives. We used the pseudo-Euclidean distance to introduce a threshold to handle real data imperfection and speed up the process. Simulations using our proposed fitness functions and a fitness function based on inside-outside function show that our fitness function based on the pseudo-Euclidean distance performs better.