HeightLane: BEV Heightmap guided 3D Lane Detection
Chaesong Park, Eunbin Seo, Jongwoo Lim

TL;DR
HeightLane introduces a novel monocular 3D lane detection method that predicts detailed ground height maps and uses a BEV feature transformation framework, significantly improving detection accuracy in complex environments.
Contribution
The paper presents HeightLane, a new approach that predicts height maps from monocular images and employs a deformable attention framework for accurate 3D lane detection, addressing limitations of ground modeling.
Findings
Achieves state-of-the-art F-score on OpenLane validation set.
Effectively models complex road slopes with multi-slope height prediction.
Utilizes LiDAR data to generate ground truth height maps for training.
Abstract
Accurate 3D lane detection from monocular images presents significant challenges due to depth ambiguity and imperfect ground modeling. Previous attempts to model the ground have often used a planar ground assumption with limited degrees of freedom, making them unsuitable for complex road environments with varying slopes. Our study introduces HeightLane, an innovative method that predicts a height map from monocular images by creating anchors based on a multi-slope assumption. This approach provides a detailed and accurate representation of the ground. HeightLane employs the predicted heightmap along with a deformable attention-based spatial feature transform framework to efficiently convert 2D image features into 3D bird's eye view (BEV) features, enhancing spatial understanding and lane structure recognition. Additionally, the heightmap is used for the positional encoding of BEV…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Advanced Neural Network Applications · Image and Object Detection Techniques
MethodsSparse Evolutionary Training · Spatial Feature Transform
