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

arXiv:2609.26039 · cs.CV, cs.AI, cs.LG · Submitted 2026-09-22 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-09-22

Updated: 2026-09-22

Comments: 26 pages, 11 figures

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: Point cloud generation has emerged as a crucial task for accurately capturing and reproducing the complexity of the physical world.

Terminology

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.

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