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arXiv · 2607.01370

MapDreamer: Aerial Imagery Conditioned Latent Diffusion for Lane-Level Map Generation

Julian Brandes·Philipp Crocoll·Wolfram Burgard
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Abstract

High definition map generation is essential for autonomous driving, yet remains a labor-intensive process at scale. We present MapDreamer, a generative diffusion model that synthesizes lane-level vector maps with explicit topology directly from a single aerial image. MapDreamer learns a compact latent representation of lane centerlines and their topological relations using a variational autoencoder and predicts graphs with a transformer-based latent diffusion model. To align generated maps with the observed scene, we condition each denoising step on dense aerial features injected through cross-attention. To handle the varying number of lanes across scenes, we propose a lane cardinality module paired with background ghost lane latents, a learned buffer that prevents slot collapse during diffusion. Furthermore, we introduce a sliding-window global graph aggregation strategy that stitches local tiles into city-scale maps while preserving connectivity through encoded lane boundaries. Experiments on UrbanLaneGraph derived from Argoverse 2 show improved geometric and topological fidelity over non-generative baselines.

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Julian Brandes, Philipp Crocoll, Wolfram Burgard. 2026-07-01. MapDreamer: Aerial Imagery Conditioned Latent Diffusion for Lane-Level Map Generation. https://arxiv.org/abs/2607.01370

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