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LanePtrNet: Revisiting Lane Detection as Point Voting and Grouping on Curves
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.
Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds
Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications.
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