SuperFlex: Deformable Superquadrics for Point Cloud Decomposition

Superquadrics have proven to provide a compact, geometri-cally meaningful representation for 3D objects. However, existing meth-ods suffer from limited reconstruction accuracy, are restricted to rigidprimitives, and lack robustness to partial point clouds. In this work,we present SuperFlex, an enhanced framework that expands the expres-sive power and applicability of superquadric decompositions. First, weintroduce a novel loss formulation which significantly improves recon-struction accuracy. Second, we include bending and tapering deforma-tions, enabling high-fidelity representation of curved and asymmetricgeometries. Finally, we leverage these high-quality decompositions assupervision to train a model that is robust to partial real-world pointclouds. Experiments demonstrate substantial improvements in recon-struction accuracy over both optimization- and learning-based baselineswhile maintaining a highly compact primitive representation. Projectpage: https://superflex3d.github.io.Point Cloud