  We present ONCE-3DLanes, a real-world autonomous driving dataset with lane
layout annotation in 3D space. Conventional 2D lane detection from a monocular
image yields poor performance of following planning and control tasks in
autonomous driving due to the case of uneven road. Predicting the 3D lane
layout is thus necessary and enables effective and safe driving. However,
existing 3D lane detection datasets are either unpublished or synthesized from
a simulated environment, severely hampering the development of this field. In
this paper, we take steps towards addressing these issues. By exploiting the
explicit relationship between point clouds and image pixels, a dataset
annotation pipeline is designed to automatically generate high-quality 3D lane
locations from 2D lane annotations in 211K road scenes. In addition, we present
an extrinsic-free, anchor-free method, called SALAD, regressing the 3D
coordinates of lanes in image view without converting the feature map into the
bird's-eye view (BEV). To facilitate future research on 3D lane detection, we
benchmark the dataset and provide a novel evaluation metric, performing
extensive experiments of both existing approaches and our proposed method. The
aim of our work is to revive the interest of 3D lane detection in a real-world
scenario. We believe our work can lead to the expected and unexpected
innovations in both academia and industry.
