发表机构
Bosch Engineering Center Cluj（博世工程中心克卢日）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶中拓扑推理的车道建模问题，提出基于三次贝塞尔曲线的TopoCurve架构，利用几何感知的注意力与监督，在OpenLane-V2上取得50.6 OLS的新SOTA。
AI 中文摘要
拓扑推理从多视角图像中联合检测3D车道和交通元素，并推断它们的结构连接性。现有方法将车道建模为离散折线，缺乏平滑性、解析切线方向和用于注意力的全局空间支持，同时提供稀疏的拓扑监督。我们提出TopoCurve，一种基于结构化参数化车道表示的几何驱动3D拓扑推理架构。车道被建模为端点固定的三次贝塞尔曲线，实现具有精确端点和解析定义方向的连续几何。我们在整个流程中利用这种共享的曲线几何。端点距离和切线对齐被编码为多尺度傅里叶特征，并注入拓扑头。采样曲线点作为几何对齐的参考，用于跨越整个车道的可变形交叉注意力。并行的曲线锚定注意力分支为多对多拓扑监督提供多样化的预测。这些组件形成一个紧密耦合的级联，其中表示使几何推理成为可能，引导特征聚合，并支持更密集的监督。TopoCurve在OpenLane-V2基准上达到50.6 OLS，无需任何后处理，在端到端纯相机方法中建立了新的最先进水平，并在端点检测上优于所有现有方法（DET_p上56.8对52.6）。
英文摘要
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).
CommentsAccepted at NeurIPS 2026 (main track). 15 pages, 4 figures
