  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.
