摘要：拓扑推理从多视角图像中联合检测 3D 车道线和交通元素，并推断它们的结构连接关系。现有方法将车道线建模为离散折线，因而缺乏平滑性、可解析的切线方向，以及可供注意力机制使用的全局空间支持；同时，它们提供的拓扑监督也较为稀疏。我们提出 TopoCurve，这是一种由几何驱动的 3D 拓扑推理架构，其基础是结构化的参数化车道表示。车道被建模为端点固定的三次 Bézier 曲线，从而实现具有精确端点和可解析方向性的连续几何表示。我们在整个流程中利用这一共享的曲线几何。端点距离和切线对齐通过多尺度 Fourier 特征进行编码，并注入拓扑预测头。采样得到的曲线点作为几何对齐的参考点，用于覆盖整条车道的可变形交叉注意力。并行的曲线锚定注意力分支为一对多的拓扑监督提供多样化预测。这些组件构成了一个紧密耦合的级联体系：表示方式支持几何推理，引导特征聚合，并支持更密集的监督。在无需任何后处理的情况下，TopoCurve 在 OpenLane-V2 基准上取得了 50.6 OLS，在端到端纯视觉方法中创下新的最佳水平；同时，其端点检测性能优于所有现有方法（DET_p 为 56.8，而此前为 52.6）。

Abstract:Topology reasoning jointly detects 3D lanes and traffic elements from multi-view images and infers their structural connectivity. Current methods model lanes as discrete polylines, lacking smoothness, analytical tangent directions, and global spatial support for attention, while providing sparse topology supervision. We propose TopoCurve, a geometry-driven architecture for 3D topology reasoning grounded in a structured parametric lane representation. Lanes are modeled as endpoint-fixed cubic Bézier curves, enabling continuous geometry with exact endpoints and analytically defined directionality. We exploit this shared curve geometry across the entire pipeline. Endpoint distance and tangent alignment are encoded with multi-scale Fourier features and injected into the topology head. Sampled curve points serve as geometry-aligned references for deformable cross-attention spanning the full lane. Parallel curve-anchored attention branches provide diverse predictions for one-to-many topology supervision. These components form a tightly coupled cascade where representation enables geometric reasoning, guides feature aggregation, and supports denser supervision. TopoCurve achieves 50.6 OLS on the OpenLane-V2 benchmark without any post-processing, establishing a new state-of-the-art among end-to-end camera-only methods, and outperforms all existing approaches on endpoint detection (56.8 vs. 52.6 on DET_p).