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.

本文提出了一种新颖的 LanePtrNet 方法，将车道检测视为有序集上的点投票和分组过程，通过将骨干特征作为输入并预测曲线感知的中心性来表示每条车道并分配最可能的中心点。通过利用局部邻域的特征和跨实例注意力得分，我们设计了一个分组模块，进一步在相邻和播种点之间执行车道聚类。我们的方法还可以作为背骨的替代方案，从而轻松扩展到 3D 车道检测任务。通过进行综合实验，验证了我们提出的方法的有效性，并展示了其卓越的性能。