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About this paper
Lei Yang, Kaicheng Yu, Tao Tang, Jun Li, Kun Yuan, Li Wang, Xinyu Zhang, Peng Chen
BEVHeight presents a robust framework for vision-based roadside 3D object detection by transitioning from depth-based to height-based feature projection. The method achieves state-of-the-art accuracy on roadside benchmarks and demonstrates superior resilience to extrinsic camera parameter disturbances.
Key insights
- Height to the ground offers a more stable and distance-agnostic geometric property for high-mounted roadside cameras compared to depth from the camera, leading to more robust 3D object detection.
- Dynamic discretization of height bins improves optimization and performance by adaptively concentrating bins in critical height ranges.
- Explicit LiDAR point cloud supervision for 3D feature lifting does not provide significant performance gains in roadside scenarios, suggesting different supervision strategies may be needed for infrastructure-based perception.
Summary by alphaXiv.
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