STONE: A Submodular Optimization Framework for Active 3D Object Detection
<|reference_start|>STONE: A Submodular Optimization Framework for Active 3D Object Detection: 3D object detection is fundamentally important for various emerging applications, including autonomous driving and robotics. A key requirement for training an accurate 3D object detector is the availability of a large amount of LiDAR-based point cloud data. Unfortunately, labeling point cloud data is extremely challenging, as accurate 3D bounding boxes and semantic labels are required for each potential object. This paper proposes a unified active 3D object detection framework, for greatly reducing the labeling cost of training 3D object detector. Our framework is based on a novel formulation of submodular optimization, specifically tailored to the problem of active 3D object detection. In particular, we address two fundamental challenges associated with active 3D object detection: data imbalance and the need to cover the distribution of the data, including LiDAR-based point cloud data of varying difficulty levels. Extensive experiments demonstrate that our method achieves state-of-the-art performance with high computational efficiency compared to existing active learning methods.<|reference_end|>
@article{mao2024stone:,
    title={STONE: A Submodular Optimization Framework for Active 3D Object
  Detection},
    author={Ruiyu Mao, Sarthak Kumar Maharana, Rishabh K Iyer, Yunhui Guo},
    journal={arXiv preprint arXiv:2410.03918},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03918},
    primaryClass={cs.CV}
}