将图元组织成语义部件：用于3D分割的强化推理
Organize Primitives into Semantic Parts: Reinforcement Reasoning for 3D Segmentation
- Nanjing University（南京大学）
- University of New South Wales（新南威尔士大学）
- Australian National University（澳大利亚国立大学）
- University of Technology Sydney（悉尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出RePart，将图元到语义部件的组织建模为有限时域马尔可夫决策过程，通过轨迹级强化推理优化合并与停止策略，结合边界感知表面标记映射回网格，在PartNet和3DCoMPaT++上取得领先的分割性能。
AI中文摘要：
基于图元的3D分割为密集表面预测提供了一种紧凑且明确的替代方案，自然支持结构抽象和边界定位。然而，几何分解本身并不能确定图元应如何组织成语义部件：一个部件可能跨越多个图元，而几何相似或接触的图元可能属于不同的部件。因此，我们引入了RePart（强化部件推理），它将图元到部件的组织问题形式化为一个有限时域的马尔可夫决策过程，并通过轨迹级强化推理来学习语义组织。RePart从细粒度的超二次曲面构建了一个可组合图元工作空间，并应用了一种合并并停止策略，该策略的决策由其对最终分区的下游影响而非局部图元兼容性来优化。推断出的部件身份随后通过边界感知表面标记映射回原始网格，从而在超出图元近似的范围内保持准确的表面边界。在PartNet上，RePart在所有四个聚合分区指标上取得了最强结果；在3DCoMPaT++上，它在无需目标数据集微调的情况下获得了最高的RI和SC。这些结果表明，强化推理为将几何图元组织成语义部件提供了一种有效机制，同时保持了密集分割的准确性。代码可在以下网址获取：https://this URL。
英文摘要：
Primitive-based 3D segmentation offers a compact and explicit alternative to dense surface prediction, naturally supporting structural abstraction and boundary localization. However, geometric decomposition alone does not determine how primitives should be organized into semantic parts: a single part may span multiple primitives, while geometrically similar or touching primitives may belong to different parts. We therefore introduce RePart (Reinforcement Part Reasoning), which formulates primitive-to-part organization as a finite-horizon Markov decision process and learns semantic organization through trajectory-level reinforcement reasoning. RePart constructs a Composable Primitive Workspace from fine-grained superquadrics and applies a merge-and-stop policy whose decisions are optimized by their downstream effects on the resulting partition rather than local primitive compatibility. The inferred part identities are then mapped back to the original mesh through Boundary-Aware Surface Labeling, preserving accurate surface boundaries beyond the primitive approximation. On PartNet, RePart achieves the strongest results across all four aggregate partition metrics; on 3DCoMPaT++, it obtains the highest RI and SC without target-dataset fine-tuning. These results demonstrate that reinforcement reasoning provides an effective mechanism for organizing geometric primitives into semantic parts while retaining dense segmentation accuracy. Code is available at https://github.com/EngineeringAI-LAB/RePart.
