Probabilistic Superquadrics fitting to point clouds

version 1.0.1 (785 KB) by Weixiao Liu
An algorithm to recover a superquadric surface/primitive from a given point cloud.

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Updated 17 May 2022

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An algorithm to recover a superquadric surface/primitive from a given point cloud, with good robustness, accuracy and efficiency. The superquadric abstracted from a point cloud provides a concise, volumetric, and geometrically meaningful interpretation of objects and environment. It can work as a low-level volumetric representation, from which higher level tasks, e.g., motion planning, collision detection and robot-environment interaction, can be built up.
Source code and utility functions are in /src; demos are included in the /example_scripts.
Other classical methods are also included in this package [1-4].
For any further implementation details, you are welcomed to visit https://github.com/bmlklwx/EMS-superquadric_fitting and start an issue.
If you find this package usefule, please cite:
W. Liu, Y. Wu, S. Ruan, G. S. Chirikjian, Robust and Accurate Superquadric Recovery: a Probabilistic Approach, CVPR 2022.
Reference:
[1] F. Solina and R. Bajcsy. Recovery of parametric models fromr ange images: the case for superquadrics with global defomations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(2):131–147, 1990
[2] A. D. Gross and T. E. Boult. Error of fit measures for recovering parametric solids. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 690–694, 1988
[3] Y. Hu and W. G. Wee. Robust 3D part extraction from range images with deformable superquadric models. In Signal Processing, Sensor Fusion, and Target Recognition IV, volume 2484, pages 524 – 535. International Society for Optics and Photonics, SPIE, 1995
[4] N. Vaskevicius and A. Birk. Revisiting superquadric fitting: A numerically stable formulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1):220–233, 2019

Cite As

Weixiao Liu (2022). Probabilistic Superquadrics fitting to point clouds (https://www.mathworks.com/matlabcentral/fileexchange/111745-probabilistic-superquadrics-fitting-to-point-clouds), MATLAB Central File Exchange. Retrieved .

W. Liu, Y. Wu, S. Ruan, G. Chirikjian, Robust and Accurate Superquadric Recovery: a Probabilistic Approach, CVPR 2022

MATLAB Release Compatibility
Created with R2022a
Compatible with any release
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