GroupLane: End-to-End 3D Lane Detection With Channel-Wise Grouping

Abstract

Efficiency is quite important for 3D lane detection while previous detectors are either computationally expensive or difficult for optimization. To bridge this gap, we propose a fully convolutional detector named GroupLane, which is simple, fast, and still maintains high detection precision. Specifically, we first propose to split extracted feature into multiple groups along the channel dimension and employ every group to represent a prediction. In this way, GroupLane realizes end-to-end detection like DETR based on pure convolutional neural networks. Then, we propose to represent lanes by performing row-wise classification in bird's eye view and devise a set of detection heads. Compared with existing row-wise classification implementations that only support recognizing vertical lanes, ours can detect both vertical and horizontal ones. Additionally, a matching algorithm named single-win one-to-one matching is developed to associate predictions with labels during training. Extensive experiments are conducted to verify the effectiveness of the proposed strategies, and the results suggest that GroupLane achieves the best performance with high inference speed on both the popular OpenLane and Once-3DLanes benchmarks. In addition, GroupLane is the first fully convolutional 3D lane detector that achieves end-to-end detection without post-processing.