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

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[Submitted on 1 Jul 2026]

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

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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.

Comments: Accepted at ECCV 2026

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

ACM classes: I.2.10

Cite as: arXiv:2607.01370 [cs.CV]

(or arXiv:2607.01370v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.01370

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Julian Brandes [view email] [v1] Wed, 1 Jul 2026 18:33:38 UTC (31,576 KB)

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