3D Lane Detection
17 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 )
Benchmarks
These leaderboards are used to track progress in 3D Lane Detection
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OpenLane
ONCE-3DLanes
OpenLane-V2 val
OpenLane-V2 test
Most implemented papers
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
OpenDriveLab/PersFormer_3DLane
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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
once-3dlanes/once_3dlanes_benchmark
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CVPR 2022
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
VectorMapNet: End-to-end Vectorized HD Map Learning
Mrmoore98/VectorMapNet_code
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To the best of our knowledge, VectorMapNet is the first work designed towards end-to-end vectorized map learning from onboard observations.
3D-LaneNet: End-to-End 3D Multiple Lane Detection
yuliangguo/Pytorch_Generalized_3D_Lane_Detection
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ICCV 2019
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
yuliangguo/Pytorch_Generalized_3D_Lane_Detection
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ECCV 2020
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
liuruijin17/clgo
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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
megvii-research/petr
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ICCV 2023
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
hustvl/maptr
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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
HERO-MDH/3DLaneNAS
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International Conference on Artificial Neural Networks 2022 2022
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.
