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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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Mitsouras, I., Chaidos, N., Stamou, G., & Voulodimos, A. (2026). EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion. https://omanscience.com/en/articles/emerge-resolution-agnostic-point-cloud-generation-with-equivariant-graph-based-diffusion
MLA 9
Mitsouras, Ilias, et al. "EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion." https://omanscience.com/en/articles/emerge-resolution-agnostic-point-cloud-generation-with-equivariant-graph-based-diffusion.
Chicago (author–date)
Mitsouras, Ilias, Nikolaos Chaidos, Giorgos Stamou, and Athanasios Voulodimos. 2026. "EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion." https://omanscience.com/en/articles/emerge-resolution-agnostic-point-cloud-generation-with-equivariant-graph-based-diffusion.
Harvard
Mitsouras, I., Chaidos, N., Stamou, G. and Voulodimos, A. (2026) 'EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion', Available at: https://omanscience.com/en/articles/emerge-resolution-agnostic-point-cloud-generation-with-equivariant-graph-based-diffusion.
Vancouver
Mitsouras I, Chaidos N, Stamou G, Voulodimos A. EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion. https://omanscience.com/en/articles/emerge-resolution-agnostic-point-cloud-generation-with-equivariant-graph-based-diffusion
IEEE
I. Mitsouras, N. Chaidos, G. Stamou, and A. Voulodimos, "EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion," https://omanscience.com/en/articles/emerge-resolution-agnostic-point-cloud-generation-with-equivariant-graph-based-diffusion.