用3D生成模型的结构先验实现高效零件分割，仅需少量标注数据。
SegviGen: Repurposing 3D Generative Model for Part Segmentation
- 利用预训练3D生成模型的结构先验，通过颜色编码实现零件分割
- 交互式分割性能提升40%，全量分割提升15%，仅用0.32%标注数据
- 支持交互、全量及2D引导分割，适合少样本场景
我们提出SegviGen，一种将预训练3D生成模型用于3D零件分割的新框架。现有方法或通过蒸馏/多视角掩码聚合引入强2D先验，导致视图间不一致和边界模糊；或依赖大规模标注3D数据进行判别式分割，成本高昂。SegviGen则利用预训练3D生成模型中编码的结构先验，通过在几何对齐重建的活跃体素上预测部件指示性颜色，实现高效分割。该框架统一支持交互式、全量及2D引导下的全量分割。大量实验表明，其在交互式分割上优于当前最佳方法40%，全量分割提升15%，且仅需0.32%的标注训练数据。结果证明，预训练3D生成先验可有效迁移至3D零件分割任务，实现低监督下的强性能表现。
原文摘要 · Abstract (English)
We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D priors into 3D via distillation or multi-view mask aggregation, often suffering from cross-view inconsistency and blurred boundaries, or explore native 3D discriminative segmentation, which typically requires large-scale annotated 3D data and substantial training resources. In contrast, SegviGen leverages the structured priors encoded in pretrained 3D generative model to induce segmentation through distinctive part colorization, establishing a novel and efficient framework for part segmentation. Specifically, SegviGen encodes a 3D asset and predicts part-indicative colors on active voxels of a geometry-aligned reconstruction. It supports interactive part segmentation, full segmentation, and full segmentation with 2D guidance in a unified framework. Extensive experiments show that SegviGen improves over the prior state of the art by 40% on interactive part segmentation and by 15% on full segmentation, while using only 0.32% of the labeled training data. It demonstrates that pretrained 3D generative priors transfer effectively to 3D part segmentation, enabling strong performance with limited supervision. See our project page at https://fenghora.github.io/SegviGen-Page/.
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