Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
<|reference_start|>Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?: Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training. However, collecting such large-scale densely-supervised datasets is notoriously costly. To reduce the cumbersome data annotation process, we propose a novel sparsely-annotated framework, in which we just annotate one 3D object per scene. Such a sparse annotation strategy could significantly reduce the heavy annotation burden, while inexact and incomplete sparse supervision may severely deteriorate the detection performance. To address this issue, we develop the SS3D++ method that alternatively improves 3D detector training and confident fully-annotated scene generation in a unified learning scheme. Using sparse annotations as seeds, we progressively generate confident fully-annotated scenes based on designing a missing-annotated instance mining module and reliable background mining module. Our proposed method produces competitive results when compared with SOTA weakly-supervised methods using the same or even more annotation costs. Besides, compared with SOTA fully-supervised methods, we achieve on-par or even better performance on the KITTI dataset with about 5x less annotation cost, and 90% of their performance on the Waymo dataset with about 15x less annotation cost. The additional unlabeled training scenes could further boost the performance. The code will be available at https://github.com/gaocq/SS3D2.<|reference_end|>
@article{gao2024are,
    title={Are Dense Labels Always Necessary for 3D Object Detection from Point
  Cloud?},
    author={Chenqiang Gao, Chuandong Liu, Jun Shu, Fangcen Liu, Jiang Liu, Luyu
  Yang, Xinbo Gao, and Deyu Meng},
    journal={arXiv preprint arXiv:2403.02818},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02818},
    primaryClass={cs.CV}
}