ONCE-3DLanes: Building Monocular 3D Lane Detection
This work addresses the problem of enabling effective and safe autonomous driving by providing a real-world dataset and method for 3D lane detection, which is incremental as it builds on existing 2D and 3D lane detection efforts.
The authors tackled the lack of real-world datasets for monocular 3D lane detection by introducing ONCE-3DLanes, a dataset with 211K annotated road scenes, and proposed SALAD, an anchor-free method that regresses 3D lane coordinates directly in image view, achieving competitive performance in benchmarks.
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 However, existing 3D lane detection datasets are either unpublished or synthesized from a simulated environment, severely hampering the development of this field.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.