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About this paper
Ruqin Zhou, Chenguang Dai, Wanshou Jiang, Yongsheng Zhang, Hanyun Wang, San Jiang
SuperMapNet is a multi-modal framework that processes camera images and LiDAR point clouds to construct high-definition vectorized maps with extended perception range and improved geometric accuracy. The method achieves a mean Average Precision of 66.5% on the nuScenes dataset (hard setting), an improvement of 14.9 mAP over the previous state-of-the-art, and effectively extends the perception range to 120 meters.
Key insights
- Explicitly coupling semantic information from cameras and geometric data from LiDAR through synergy enhancement and disparity alignment significantly improves BEV feature quality and extends perception range.
- Modeling map elements with three-level interactions (Point2Point, Element2Element, Point2Element) is crucial for capturing local geometric consistency, global semantic relationships, and the interplay between elements, leading to higher construction accuracy.
- The combined application of robust multi-modal feature fusion and comprehensive element interaction modeling is essential for achieving both long-range perception and centimeter-level accuracy in vectorized HD maps.
Summary by alphaXiv.
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