SuperFlex: Deformable Superquadrics for Point Cloud Decomposition
2026-07-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed SuperFlex, a new method to better represent 3D shapes using superquadrics, which are simple geometric forms. They improved the accuracy of shape reconstruction by introducing a new way to measure errors and added features like bending and tapering to handle curved and uneven shapes. Their method also works well with incomplete 3D data from real-world scans. Tests showed that SuperFlex outperforms earlier techniques while keeping the shape descriptions compact.
superquadrics3D reconstructionpoint cloudsgeometric representationloss functionbending deformationtapering deformationmodel robustnessoptimizationlearning-based methods
Authors
Gabriel Tavernini, Elisabetta Fedele, Tiago Novello, Leonidas Guibas, Marc Pollefeys, Francis Engelmann
Abstract
Superquadrics have proven to provide a compact, geometrically meaningful representation for 3D objects. However, existing methods suffer from limited reconstruction accuracy, are restricted to rigid primitives, and lack robustness to partial point clouds. In this work, we present SuperFlex, an enhanced framework that expands the expressive power and applicability of superquadric decompositions. First, we introduce a novel loss formulation which significantly improves reconstruction accuracy. Second, we include bending and tapering deformations, enabling high-fidelity representation of curved and asymmetric geometries. Finally, we leverage these high-quality decompositions as supervision to train a model that is robust to partial real-world point clouds. Experiments demonstrate substantial improvements in reconstruction accuracy over both optimization- and learning-based baselines while maintaining a highly compact primitive representation.