基于层级八叉树的3D形状生成，通过部件-整体信息传递提升细节与效率。
HierOctFusion: Multi-scale Octree-based 3D Shape Generation via Part-Whole-Hierarchy Message Passing
- 采用多尺度八叉树结构，融合部件-整体层次消息传递机制。
- 在ShapeNet上生成质量优于基线方法，且计算效率更高。
- 适合需要高细节、稀疏结构的3D内容生成任务。
3D内容生成因数据固有的结构复杂性而仍具挑战。尽管近期基于八叉树的扩散模型在效率与质量间取得平衡，但通常将3D对象视为整体实体，忽略其语义部件层级，限制了泛化能力；同时，高分辨率整体建模计算开销大，而真实物体本质稀疏且具有层次性，更适合分层生成。为此，我们提出HierOctFusion，一种具备部件感知能力的多尺度八叉树扩散模型，通过增强层级特征交互，生成精细且稀疏的物体结构。此外，引入跨注意力条件机制，将部件级信息注入生成过程，实现从部件到整体的语义特征有效传播。我们还利用预训练分割模型构建了一个带部件类别标注的3D数据集，以支持训练与评估。实验表明，HierOctFusion在生成质量与效率方面均优于现有方法。
原文摘要 · Abstract (English)
3D content generation remains a fundamental yet challenging task due to the inherent structural complexity of 3D data. While recent octree-based diffusion models offer a promising balance between efficiency and quality through hierarchical generation, they often overlook two key insights: 1) existing methods typically model 3D objects as holistic entities, ignoring their semantic part hierarchies and limiting generalization; and 2) holistic high-resolution modeling is computationally expensive, whereas real-world objects are inherently sparse and hierarchical, making them well-suited for layered generation. Motivated by these observations, we propose HierOctFusion, a part-aware multi-scale octree diffusion model that enhances hierarchical feature interaction for generating fine-grained and sparse object structures. Furthermore, we introduce a cross-attention conditioning mechanism that injects part-level information into the generation process, enabling semantic features to propagate effectively across hierarchical levels from parts to the whole. Additionally, we construct a 3D dataset with part category annotations using a pre-trained segmentation model to facilitate training and evaluation. Experiments demonstrate that HierOctFusion achieves superior shape quality and efficiency compared to prior methods.
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