Density-Aware Point Cloud Upsampling Via Relational Graph Flow Matching
and Shicai Fan
AI summary
Problem
Conventional point cloud upsampling methods treat all regions homogeneously, causing over-smoothing in sparse areas and redundant computation in dense ones, while diffusion-based alternatives are too computationally heavy for real-time use.
Approach
PURF partitions point clouds into dense and sparse regions using an adaptive relational graph, then employs a transformer-based flow matching model to predict velocity fields for rapid, few-step upsampling.
Key results
- Achieves state-of-the-art or competitive Chamfer and Hausdorff distances on PUGAN and PU1K benchmarks
- Demonstrates robust generalization across varying sensor distances in KITTI and different spatial scales in a proprietary campus dataset
- Reduces inference steps and computational overhead compared to iterative diffusion-based upsampling methods
- Preserves fine geometric details and minimizes outliers in sparse regions where homogeneous methods fail
Why it matters
Provides a computationally efficient and geometrically accurate upsampling solution critical for real-time 3D perception in autonomous driving and robotics.
Abstract
Real-world point clouds exhibit non-uniform density distributions, varying across distance and scale. Conventional up- sampling methods typically treat points homogeneously, which over-smooths sparse regions while over-processing dense regions. We propose PURF, a density-aware point cloud upsampling frame- work based on relational graph flow matching. PURF leverages a heterogeneous graph representation to capture density variations through relational graph construction, and employs transformer- based flow matching to predict timestep-dependent velocity fields. This design enables a density-aware and efficient mapping from sparse inputs to dense point clouds, reducing computational over- head compared to recent approaches. Extensive experiments on synthetic, KITTI, and a proprietary campus dataset collected by our team demonstrate that PURF achieves advanced performance in upsampling point clouds qualitatively and quantitatively.