SOURCE-LINKED INTELLIGENCE

GridFlow: Structured Latent Flow for Seamless City-Scale 3D Point Cloud Generation

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Generating realistic 3D city environments from remote sensing data is important for simulation, urban planning, and mixed reality, yet existing point cloud generation methods are limited to single objects or bounded indoor scenes and cannot handle the scale, seamless tiling, and partial observability challenges of city-scale generation. We present \ours{}, a multi-stage framework that generates dense, colored point clouds ($10^5$ points per $150\text{m}{\times}150\text{m}$ tile) at city scale, conditioned on satellite imagery, semantic segmentation maps, and digital surface models (DSM). A \em

Read original source ↗ Open in workspace

recordType

paper

region

Global