  Methods for 3D lane detection have been recently proposed to address the
issue of inaccurate lane layouts in many autonomous driving scenarios
(uphill/downhill, bump, etc.). Previous work struggled in complex cases due to
their simple designs of the spatial transformation between front view and
bird's eye view (BEV) and the lack of a realistic dataset. Towards these
issues, we present PersFormer: an end-to-end monocular 3D lane detector with a
novel Transformer-based spatial feature transformation module. Our model
generates BEV features by attending to related front-view local regions with
camera parameters as a reference. PersFormer adopts a unified 2D/3D anchor
design and an auxiliary task to detect 2D/3D lanes simultaneously, enhancing
the feature consistency and sharing the benefits of multi-task learning.
Moreover, we release one of the first large-scale real-world 3D lane datasets:
OpenLane, with high-quality annotation and scenario diversity. OpenLane
contains 200,000 frames, over 880,000 instance-level lanes, 14 lane categories,
along with scene tags and the closed-in-path object annotations to encourage
the development of lane detection and more industrial-related autonomous
driving methods. We show that PersFormer significantly outperforms competitive
baselines in the 3D lane detection task on our new OpenLane dataset as well as
Apollo 3D Lane Synthetic dataset, and is also on par with state-of-the-art
algorithms in the 2D task on OpenLane. The project page is available at
https://github.com/OpenPerceptionX/PersFormer_3DLane and OpenLane dataset is
provided at https://github.com/OpenPerceptionX/OpenLane.
