提出几何感知的车道线检测与拓扑推理框架，提升自动驾驶地图构建精度。
GeoReFormer: Geometry-Aware Refinement for Lane Segment Detection and Topology Reasoning
- 基于几何先验初始化查询，显式建模车道线连续性
- 在坐标空间内约束优化，稳定生成多段车道线
- 按需传播拓扑关系，提升复杂路口一致性
准确的3D车道线段检测与拓扑推理对自动驾驶中结构化在线地图构建至关重要。现有基于Transformer的方法将此任务视为基于查询的集合预测，但大多沿用为紧凑目标检测设计的解码器。然而，车道线是嵌入于有向图中的连续折线，通用查询初始化和无约束优化无法显式编码其几何与关系结构。本文提出GeoReFormer（几何感知精炼变换器），一种统一的基于查询架构，将几何与拓扑先验直接嵌入Transformer解码器。GeoReFormer引入数据驱动的几何先验用于结构化查询初始化，采用受限坐标空间精炼以实现稳定的折线变形，并通过每查询门控拓扑传播机制选择性整合关系上下文。在OpenLane-V2基准上，GeoReFormer达到34.5% mAP的最优性能，同时显著提升拓扑一致性，验证了显式编码几何与关系结构的有效性。
原文摘要 · Abstract (English)
Accurate 3D lane segment detection and topology reasoning are critical for structured online map construction in autonomous driving. Recent transformer-based approaches formulate this task as query-based set prediction, yet largely inherit decoder designs originally developed for compact object detection. However, lane segments are continuous polylines embedded in directed graphs, and generic query initialization and unconstrained refinement do not explicitly encode this geometric and relational structure. We propose GeoReFormer (Geometry-aware Refinement Transformer), a unified query-based architecture that embeds geometry- and topology-aware inductive biases directly within the transformer decoder. GeoReFormer introduces data-driven geometric priors for structured query initialization, bounded coordinate-space refinement for stable polyline deformation, and per-query gated topology propagation to selectively integrate relational context. On the OpenLane-V2 benchmark, GeoReFormer achieves state-of-the-art performance with 34.5% mAP while improving topology consistency over strong transformer baselines, demonstrating the utility of explicit geometric and relational structure encoding.
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