Skip to content

An efficient and robust probabilistic approach for fitting superellipse to point clouds.

License

Notifications You must be signed in to change notification settings

bmlklwx/Robust-superellipse-fitting

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

A Robust Superellipse Fitting Algorithm

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.

superquadrics1superquadrics2superquadrics3

superquadrics4superquadrics5superquadrics6

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.

About

An efficient and robust probabilistic approach for fitting superellipse to point clouds.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages