发表机构
National Technical University of Athens（雅典国立技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EMERGE提出首个完全SE(3)等变的图扩散点云生成模型，实现分辨率无关的零样本推理，在标准指标上达到最先进质量并显著加速训练收敛。
AI 中文摘要
点云生成已成为准确捕捉和再现物理世界复杂性的关键任务。然而，现有的生成方法主要依赖Transformer和变分自编码器（VAE），常常忽略3D空间固有的连续、非网格拓扑结构。尽管图结构的集成已在相关的判别性视觉任务中带来显著益处，但此类几何架构在3D生成建模中仍明显缺失。为解决这一空白，我们提出了EMERGE（用于分辨率无关点云生成的等变多尺度图神经网络），这是第一个完全$SE(3)$等变的基于图的扩散主干网络，专门设计用于在保持连续空间对称性的同时生成点云。我们的框架绕过了标准生成流程中刚性的分辨率依赖，支持在多个任意空间分辨率下进行零样本推理。大量实证评估表明，EMERGE在标准指标上达到了最先进的生成质量，同时强大的固有几何归纳偏置使其与现有基线方法相比，训练收敛速度显著加快。
英文摘要
Point cloud generation has emerged as a crucial task for accurately capturing and reproducing the complexity of the physical world. However, existing generative approaches, predominantly relying on Transformers and Variational Autoencoders (VAEs), frequently ignore the continuous, non-grid topologies inherent to 3D spaces. Although the integration of graph-based structures has yielded significant benefits in related discriminative vision tasks, such geometric architectures remain noticeably absent from 3D generative modeling. To address this gap, we introduce EMERGE (Equivariant Multi-scale GNN for Resolution-agnostic point cloud GEneration), the first fully $SE(3)$-equivariant graph-based diffusion backbone explicitly designed to generate point clouds while preserving continuous spatial symmetries. Our framework bypasses the rigid resolution dependencies of standard generative pipelines, enabling zero-shot inference at multiple, arbitrary spatial resolutions. Extensive empirical evaluations demonstrate that EMERGE achieves State-of-the-Art generation quality across standard metrics, while the strong inherent geometric inductive biases enable significantly faster training convergence compared to existing baseline methods.
Comments26 pages, 11 figures
