LanePtrNet：重新将车道线检测视为曲线上的点投票与分组问题
LanePtrNet: Revisiting Lane Detection as Point Voting and Grouping on Curves
AI总结：
针对现有车道线检测方法需人工调整、后处理复杂且灵活性不足的问题，提出LanePtrNet，将检测转化为曲线上的点投票分组过程，适配点云骨干网络，可扩展至3D检测，实验验证其性能更优。
摘要：
车道线检测在自动驾驶领域发挥着关键作用。主流方法通常采用目标检测和分割任务中的基础概念（锚框、关键点等），但这类方法需要针对曲线形物体进行人工调整，需要在预定义锚框上执行穷举搜索，还需复杂的后处理步骤，应用于真实场景时可能缺乏灵活性。\n本文提出了一种新方法LanePtrNet，将车道线检测视为有序集合上的点投票与分组过程：该方法以骨干网络提取的特征为输入，预测感知曲线的中心度，将每条车道线表示为一个点，并为其分配概率最高的中心点。本文提出了一种新的点采样方法，可基于收到的投票生成候选点集合。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，进一步在邻点与种子点之间执行逐车道聚类。此外，该方法可适配基于点的框架（如PointNet++系列）作为骨干网络的替代方案，这种灵活性使其能够轻松扩展到3D车道线检测任务。我们开展了全面实验以验证所提方法的有效性，结果表明其性能更优。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
