Problem
Research questions and friction points this paper is trying to address.
Point Cloud Generation
Equivariant
Graph-based Diffusion
EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion
🤖 AI Summary
本文提出EMERGE，一种基于图的扩散模型，用于生成点云，解决了现有方法忽视3D空间连续性的问题，实现了任意分辨率下的高质量点云生成。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Equivariant Graph-based Diffusion
Resolution-agnostic Point Cloud Generation
Continuous Spatial Symmetries
SE(3)-equivariance
Faster Training Convergence
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