EMERGE:首个SE(3)等变图扩散模型实现分辨率无关点云生成

该框架支持在多个任意空间分辨率下零样本推理，无需为每个分辨率重新训练。论文报告称其在标准生成指标上达到 State-of-the-Art，且凭借几何归纳偏置，训练收敛速度明显快于现有基线方法。

EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion

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.

This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include EMERGE, diffusion models, graph neural networks.

Source attribution: curated from arXiv cs.AI (@Ilias Mitsouras, Nikolaos Chaidos, Giorgos Stamou, Athanasios Voulodimos).