A 2D version of the EMS algorithm: [CVPR 2022] Robust and Accurate Superquadric Recovery: a Probabilistic Approach.
Robust and Accurate Superquadric Recovery: a Probabilistic Approach
Weixiao Liu, Yuwei Wu, Sipu Ruan, Gregory S. Chirikjian
Our original work is to fit superquadrics (3D generalization of superellipse) to point clouds. This is a simple variant to the original paper to solve superellipse (also know as Lamé curve) fitting problem in 2D cases. The demo (test_script.m) shows the fitting results to randomly generated superellipse-shaped point clouds, with large amount of noise and outliers. This repo also contains MATLAB functions to sample points almost uniformly on the side of superellipse, and to draw superellipse.
For visitors interested in more complex 3D superquadrics fitting, please visit this repository.
If you find this repo useful, please cite
W. Liu, Y. Wu, S. Ruan and G. S. Chirikjian, "Robust and Accurate Superquadric Recovery: a Probabilistic Approach,"
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 2666-2675,
doi: 10.1109/CVPR52688.2022.00270.