用连续场表示网格拓扑，让扩散模型生成更高质量的3D网格。
Mesh BDF: Barycentric Dominance Field for 3D Native Mesh Generation
- 提出重心主导场（BDF），将离散网格拓扑转为连续表面信号。
- 生成网格面数更多、顶点分辨率更高，且支持纹理映射。
- 可无缝接入现有扩散模型，无需修改网络结构，适合高保真3D生成。
自回归建模在原生3D网格生成中取得显著进展，因其天然适配可变长度离散数据结构。然而，自回归范式固有的约束严重限制了生成网格的质量，导致面数有限、顶点分辨率受限，且难以支持纹理。为此，我们提出重心主导场（Barycentric Dominance Field, BDF），一种定义在三角网格表面的连续表示，能优雅编码顶点拓扑连接关系。BDF通过将拓扑结构转化为连续表面信号，弥合了离散网格拓扑与连续扩散生成建模之间的根本鸿沟。作为网格的内在属性，BDF与纹理图具有强相似性，可无缝集成至现有3D扩散流程中，无需架构修改。大量实验表明，BDF使扩散模型生成的原生网格在质量、可扩展性和鲁棒性上均显著优于当前最优自回归方法。
原文摘要 · Abstract (English)
Autoregressive (AR) modeling has recently achieved remarkable progress in native 3D mesh generation, largely due to its natural ability to handle variable-length, discrete data structures. However, the inherent constraints of the AR paradigm severely restrict the generated meshes, leading to limited face counts, bounded vertex resolutions, and difficulties in supporting textures. To overcome these bottlenecks, we propose the Barycentric Dominance Field (BDF), a continuous representation defined on triangular mesh surfaces that elegantly encodes vertex topological connectivity. BDF bridges the fundamental gap between discrete mesh topology and continuous diffusion-based generative modeling by transforming connectivity into a continuous surface signal. As an intrinsic mesh property, BDF shares strong similarities with texture maps, enabling its seamless integration into existing 3D diffusion pipelines without requiring architectural modifications. Extensive experiments demonstrate that BDF empowers diffusion models to generate native meshes with significantly higher quality, greater scalability, and stronger robustness compared to state-of-the-art autoregressive methods.
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