Reproduce “Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement” on OpenResearch
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About this paper
Dong-Hee Paek, Kevin Tirta Wijaya, Seung-Hyun Kong
Researchers from KAIST developed a two-stage row-wise LiDAR lane detection network that processes 3D point cloud data for autonomous vehicles. The system achieves an F1-score of 82.74% on the K-Lane dataset and reduces computational cost by approximately 30% while improving lane detection under occlusion.
Key insights
- Predicting lane existence and location row-wise in a Bird's-Eye View (BEV) map significantly reduces computational complexity compared to pixel-wise segmentation, while maintaining geometric soundness.
- A two-stage network, consisting of initial proposal generation and subsequent refinement, enhances detection accuracy by leveraging global contextual information.
- Using an attention mechanism to correlate lane tokens allows the network to effectively infer and refine lane positions, particularly for lanes partially occluded by other objects.
Summary by alphaXiv.
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