通过分层拓扑评分先验，实现3D部件分解的精准边界与关节保留。
Hi-TOPS: Hierarchical Topology-aware Scoring Prior for 3D Part Decomposition
- 构建多尺度流动-冻结场，融合几何与拓扑线索
- 在多个基准上实现稳定可编辑的分解结果
- 无需语义标签或2D先验，适合通用3D重建任务
准确的3D部件分解需将形状分割为结构上有意义的组件，同时保持精确边界并保留关节和细长连接。现有方法常因结构尺度不匹配而失效：分离的几何证据在中等尺度最可靠，但许多流程要么过于全局忽略关节，要么过于局部对噪声敏感。我们提出Hi-TOPS，一种分层拓扑感知评分先验，将互补的内在线索整合为多分辨率的Flow-Freeze场。流动区域提供可扩展的基元覆盖支持，冻结区域则在关节和细结构附近限制生长。随后，基于TSDF的体表超二次曲面拟合器捕捉主导核心与残余表面结构，再通过SQ-to-mesh映射生成连通且边界对齐的部件。在多样化的基准测试中，Hi-TOPS实现了无需语义监督或2D基础先验的稳定、可编辑分解。
原文摘要 · Abstract (English)
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.
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