用可微分贝塞尔图直接从分割图生成道路网络，无需曲线真值。
DOGE: Differentiable Bezier Graph Optimization for Road Network Extraction
- 用贝塞尔曲线参数化道路图，实现几何可微优化。
- 在SpaceNet和CityScale上超越现有方法，达到新基准。
- 适合地图构建与遥感图像矢量化研究者使用。
从航空影像中自动提取道路网络是基础任务，但现有方法依赖折线难以刻画弯曲几何。我们认为道路几何本质上是曲线型的，提出贝塞尔图（Bézier Graph）这一可微分的参数化曲线表示。主要挑战在于难以构建向量真值（GT），我们通过将任务重构为贝塞尔图上的全局优化问题来绕过此瓶颈。所提框架DOGE直接从分割掩码学习参数化贝塞尔图，无需曲线标注。其通过交替优化两个互补模块：DiffAlign利用可微渲染持续优化几何，TopoAdapt采用离散算子改进拓扑结构。在大规模SpaceNet与CityScale基准上，该方法达到新最优性能，为生成高保真道路矢量地图提供新范式。代码与数据将公开。
原文摘要 · Abstract (English)
Automatic extraction of road networks from aerial imagery is a fundamental task, yet prevailing methods rely on polylines that struggle to model curvilinear geometry. We maintain that road geometry is inherently curve-based and introduce the Bézier Graph, a differentiable parametric curve-based representation. The primary obstacle to this representation is to obtain the difficult-to-construct vector ground-truth (GT). We sidestep this bottleneck by reframing the task as a global optimization problem over the Bézier Graph. Our framework, DOGE, operationalizes this paradigm by learning a parametric Bézier Graph directly from segmentation masks, eliminating the need for curve GT. DOGE holistically optimizes the graph by alternating between two complementary modules: DiffAlign continuously optimizes geometry via differentiable rendering, while TopoAdapt uses discrete operators to refine its topology. Our method sets a new state-of-the-art on the large-scale SpaceNet and CityScale benchmarks, presenting a new paradigm for generating high-fidelity vector maps of road networks. We will release our code and related data.
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