SuperFlex: Deformable Superquadrics for Point Cloud Decomposition, 10000x Faster Than Marching Primitives
orlitany · x · 2026-09-12
A team from ETH Zurich, Stanford, IMPA and USI Lugano announced SuperFlex, an ECCV 2026 paper (poster #15) on superquadric decomposition of point clouds for 3D scene understanding.
- Method: Adds bending and tapering parameters to superquadrics for high-fidelity representation of complex geometries; a novel loss and optimization scheme refines initial decompositions; the model trains via self-supervised joint volumetric and surface losses, with optional per-object refinement.
- Key advance: Enables direct prediction from noisy, partial real-world point clouds — previously infeasible for methods requiring full point clouds.
- Results: On ShapeNet it significantly outperforms all learning- and optimization-based baselines (SQ, CSA, EMS, etc.) on IoU; the only comparable method, Marching Primitives, uses 4x more primitives and is 10000x slower.
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