Introduces a clustering-based optimization technique for fitting superquadrics to point clouds that handles noise, outliers, and deformations with closed-form solutions and convergence proofs.
Segmentation and recovery of su- perquadric models using convolutional neural networks,
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A Holistic Method for Superquadric Fitting Using Unsupervised Clustering Analysis
Introduces a clustering-based optimization technique for fitting superquadrics to point clouds that handles noise, outliers, and deformations with closed-form solutions and convergence proofs.