HeightFormer: A Semantic Alignment Monocular 3D Object Detection Method from Roadside Perspective
<|reference_start|>HeightFormer: A Semantic Alignment Monocular 3D Object Detection Method from Roadside Perspective: The on-board 3D object detection technology has received extensive attention as a critical technology for autonomous driving, while few studies have focused on applying roadside sensors in 3D traffic object detection. Existing studies achieve the projection of 2D image features to 3D features through height estimation based on the frustum. However, they did not consider the height alignment and the extraction efficiency of bird's-eye-view features. We propose a novel 3D object detection framework integrating Spatial Former and Voxel Pooling Former to enhance 2D-to-3D projection based on height estimation. Extensive experiments were conducted using the Rope3D and DAIR-V2X-I dataset, and the results demonstrated the outperformance of the proposed algorithm in the detection of both vehicles and cyclists. These results indicate that the algorithm is robust and generalized under various detection scenarios. Improving the accuracy of 3D object detection on the roadside is conducive to building a safe and trustworthy intelligent transportation system of vehicle-road coordination and promoting the large-scale application of autonomous driving. The code and pre-trained models will be released on https://anonymous.4open.science/r/HeightFormer.<|reference_end|>
@article{liu2024heightformer:,
    title={HeightFormer: A Semantic Alignment Monocular 3D Object Detection Method
  from Roadside Perspective},
    author={Pei Liu (1) and Zihao Zhang (2) and Haipeng Liu (3) and Nanfang Zheng
  (4) and Meixin Zhu (1) and Ziyuan Pu (4) ((1) Intelligent Transportation
  Thrust, Systems Hub, The Hong Kong University of Science and Technology
  (Guangzhou), (2) School of Cyber Science and Engineering, Southeast
  University, (3) Li Auto Inc, (4) School of Transportation, Southeast
  University)},
    journal={arXiv preprint arXiv:2410.07758},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07758},
    primaryClass={cs.CV}
}