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

Generating realistic 3D city environments from remote sens-ing data is important for simulation, urban planning, and mixed reality,yet existing point cloud generation methods are limited to single ob-jects or bounded indoor scenes and cannot handle the scale, seamlesstiling, and partial observability challenges of city-scale generation. Wepresent GridFlow, a multi-stage framework that generates dense, col-ored point clouds (105 points per 150m×150m tile) at city scale, con-ditioned on satellite imagery, semantic segmentation maps, and digitalsurface models (DSM). A Grid-Aligned VAE encodes each tile into atopology-preserving latent grid where tokens correspond to fixed spatialregions, enabling spatially coherent multi-modal conditioning and com-pact latent-space edge consistency that implicitly aligns thousands ofboundary points for seamless cross-tile generation. A conditional rectifiedflow model synthesizes geometry latents from the fused multi-modal con-ditions, and an orientation-aware diffusion colorizer separately handlessatellite-visible horizontal surfaces and occluded vertical façades. To sup-port standardized evaluation, we build on public 3D data sources to in-troduce City3D-MultiGen, a benchmark of 163K densely annotated tilesfrom Melbourne and London with aligned point clouds, satellite images,semantic maps, and elevation data. Experiments show that GridFlowoutperforms adapted point cloud generation baselines across all geometrymetrics and produces visually coherent colored point clouds with seam-less boundaries over arbitrarily large urban extents. Our benchmark de-tails are available at https://huggingface.co/datasets/e32/City3D-MultiGen.