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.
Fitting undeformed su- perquadrics to range data: improving model recovery and classifi- cation,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.