用一张航拍图生成高精道路拓扑，省去人工标注
MapDreamer: Aerial Imagery Conditioned Latent Diffusion for Lane-Level Map Generation
- 通过变分自编码器学车道中心线的紧凑隐空间表示
- 在城市尺度地图上实现几何与拓扑精度优于非生成基线
- 适合自动驾驶高精地图自动化生产场景
高精地图生成对自动驾驶至关重要，但规模化仍依赖大量人工。本文提出MapDreamer，一种从单张航拍图像直接生成带有显式拓扑结构的车道级矢量地图的生成式扩散模型。该模型利用变分自编码器学习车道中心线及其拓扑关系的紧凑隐表示，并通过基于Transformer的隐扩散模型进行图生成。为使生成地图与观测场景对齐，每步去噪过程均通过交叉注意力注入密集航拍特征。针对不同场景车道数量差异，提出车道数量模块与背景虚车道隐变量（ghost lane latents），防止扩散过程中槽位坍缩。此外，引入滑动窗口全局图聚合策略，将局部瓦片拼接成城市级地图，同时通过编码的车道边界保持连通性。在基于Argoverse 2构建的UrbanLaneGraph数据集上的实验表明，其生成结果在几何与拓扑保真度上均优于非生成基线。
原文摘要 · Abstract (English)
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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