Lane detection plays a critical role in the field of autonomous driving. Prevailing methods generally adopt basic concepts (anchors, key points, etc.) from object detection and segmentation tasks, while these approaches require manual adjustments for curved objects, involve exhaustive searches on predefined anchors, require complex post-processing steps, and may lack flexibility when applied to real-world scenarios.In this paper, we propose a novel approach, LanePtrNet, which treats lane detection as a process of point voting and grouping on ordered sets: Our method takes backbone features as input and predicts a curve-aware centerness, which represents each lane as a point and assigns the most probable center point to it. A novel point sampling method is proposed to generate a set of candidate points based on the votes received. By leveraging features from local neighborhoods, and cross-instance attention score, we design a grouping module that further performs lane-wise clustering between neighboring and seeding points. Furthermore, our method can accommodate a point-based framework, (PointNet++ series, etc.) as an alternative to the backbone. This flexibility enables effortless extension to 3D lane detection tasks. We conduct comprehensive experiments to validate the effectiveness of our proposed approach, demonstrating its superior performance.

翻译：摘要：车道检测在自动驾驶领域中扮演着关键角色。当前主流方法通常借鉴目标检测和分割任务中的基本概念（如锚点、关键点等），但这些方法在处理弯曲物体时需要手动调整，涉及对预定义锚点的穷举搜索，需要复杂的后处理步骤，并且在应用于现实场景时可能缺乏灵活性。本文提出了一种新颖方法——LanePtrNet，它将车道检测视为有序集合上的点投票与分组过程：该方法以主干网络特征为输入，并预测曲线感知中心度（curve-aware centerness），将每条车道表示为一个点，并为其分配最可能的中心点。我们提出了一种新颖的点采样方法，基于投票结果生成一组候选点。通过利用局部邻域特征和跨实例注意力分数，我们设计了一个分组模块，该模块进一步在邻近点与种子点之间执行车道级聚类。此外，我们的方法能够适应基于点的框架（如PointNet++系列等）作为主干网络的替代方案。这种灵活性使得该方法可以轻松扩展到三维车道检测任务。我们通过全面实验验证了所提方法的有效性，并展示了其卓越的性能。