Graph based diffusion improves point cloud generation across resolutions
EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
Summary
Point clouds are 3D representations made of dots that capture real-world shapes. Existing methods to create these typically overlook the continuous and flexible nature of 3D space. The authors designed EMERGE, a new approach that uses graph structures respecting spatial symmetries to generate point clouds at any resolution without retraining. This method produces high-quality results and learns faster than previous models.
What this means in practice
- •For 3d graphics developers: Create flexible 3D point cloud models that can output at various resolutions without retraining for different detail levels.
- •For robotics system integrators: Generate accurate 3D spatial representations that maintain geometric consistency for robot navigation and perception tasks.
Authors
Ilias Mitsouras, Nikolaos Chaidos, Giorgos Stamou, Athanasios Voulodimos
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.