Hi-TOPS: Hierarchical Topology-aware Scoring Prior for 3D Part Decomposition
For researchers in 3D shape analysis and part decomposition, Hi-TOPS offers a novel method that addresses structural-scale mismatch, but its gains are incremental over existing approaches.
Hi-TOPS introduces a hierarchical topology-aware scoring prior that aggregates intrinsic cues into a multi-resolution Flow-Freeze field, improving 3D part decomposition by preserving articulation seams and thin structures. It achieves stable, editable decompositions across diverse benchmarks without semantic supervision or 2D foundation priors.
Accurate 3D part decomposition requires separating shapes into structurally meaningful components with precise boundaries while preserving articulation seams and thin attachments. Existing approaches often suffer from a structural-scale mismatch: geometric evidence for separation is most reliable at the meso scale, yet many pipelines operate either too globally to respect joints or too locally to remain robust to noise. We propose Hi-TOPS, a Hierarchical Topology-aware Scoring Prior that aggregates complementary intrinsic cues into a multi-resolution Flow-Freeze field. Flow regions provide expandable support for primitive coverage, while Freeze regions restrict growth near articulations and thin structures. A TSDF-guided body-surface superquadric fitter then captures dominant cores and residual surface structures, followed by SQ-to-mesh assignment for connected, boundary-aligned parts. Across diverse benchmarks, Hi-TOPS delivers stable, editable decompositions without semantic supervision or 2D foundation priors.