Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving
<|reference_start|>Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving: We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. Traditional point-based 3D object detectors often employ architectures that rely on a progressive downsampling of points. While this method effectively reduces computational demands and increases receptive fields, it will compromise the preservation of crucial non-local information for accurate 3D object detection, especially in the complex driving scenarios. To address this, we introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector by efficiently modeling longer-range inter-dependency while including only a negligible overhead. Concretely, the Cross-Cluster Shifting operation enhances the conventional design by shifting partial channels from neighboring clusters, which enables richer interaction with non-local regions and thus enlarges the receptive field of clusters. We conduct extensive experiments on the KITTI, Waymo, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD in both detection accuracy and runtime efficiency.<|reference_end|>
@article{chen2024cross-cluster,
    title={Cross-Cluster Shifting for Efficient and Effective 3D Object Detection
  in Autonomous Driving},
    author={Zhili Chen, Kien T. Pham, Maosheng Ye, Zhiqiang Shen, and Qifeng Chen},
    journal={arXiv preprint arXiv:2403.06166},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06166},
    primaryClass={cs.CV cs.RO}
}