SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

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 GN

Read original source ↗ Open in workspace

recordType

paper

region

Global