3D Lane Detection
21 papers with code • 4 benchmarks • 4 datasets
The goal of 3D Lane Detection is to perceive lanes that provide guidance for autonomous vehicles. A lane can be represented as a visible laneline or a conceptual centerline. Furthermore, a lane obtains extra attributes from the understanding of the surrounding environment.
( Image credit: OpenLane-V2 )
Most implemented papers
VectorMapNet: End-to-end Vectorized HD Map Learning
To the best of our knowledge, VectorMapNet is the first work designed towards end-to-end vectorized map learning from onboard observations.
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
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.).
ONCE-3DLanes: Building Monocular 3D Lane Detection
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
3D-LaneNet: End-to-End 3D Multiple Lane Detection
We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image.
Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection
The method, inspired by the latest state-of-the-art 3D-LaneNet, is a unified framework solving image encoding, spatial transform of features and 3D lane prediction in a single network.
Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints
In this task, the correct camera pose is the key to generating accurate lanes, which can transform an image from perspective-view to the top-view.
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
More specifically, we extend the 3D position embedding (3D PE) in PETR for temporal modeling.
MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system.
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection
Lane detection is one of the most fundamental tasks for autonomous driving.
WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels
To the best of our knowledge, WS-3D-Lane is the first try of 3D lane detection under weakly supervised setting.