MapDreamer: Aerial Imagery Conditioned Latent Diffusion For Lane Level Map Generation

High definition map generation is essential for autonomousdriving, yet remains a labor-intensive process at scale. We present Map-Dreamer, a generative diffusion model that synthesizes lane-level vec-tor maps with explicit topology directly from a single aerial image.MapDreamer learns a compact latent representation of lane centerlinesand their topological relations using a variational autoencoder and pre-dicts graphs with a transformer-based latent diffusion model. To aligngenerated maps with the observed scene, we condition each denoisingstep on dense aerial features injected through cross-attention. To handlethe varying number of lanes across scenes, we propose a lane cardinal-ity module paired with background ghost lane latents, a learned bufferthat prevents slot collapse during diffusion. Furthermore, we introduce asliding-window global graph aggregation strategy that stitches local tilesinto city-scale maps while preserving connectivity through encoded laneboundaries. Experiments on UrbanLaneGraph derived from Argoverse2 show improved geometric and topological fidelity over non-generativebaselines.