False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy
<|reference_start|>False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy: Recent studies have focused on enhancing the performance of 3D object detection models. Among various approaches, ground-truth sampling has been proposed as an augmentation technique to address the challenges posed by limited ground-truth data. However, an inherent issue with ground-truth sampling is its tendency to increase false positives. Therefore, this study aims to overcome the limitations of ground-truth sampling and improve the performance of 3D object detection models by developing a new augmentation technique called false-positive sampling. False-positive sampling involves retraining the model using point clouds that are identified as false positives in the model's predictions. We propose an algorithm that utilizes both ground-truth and false-positive sampling and an algorithm for building the false-positive sample database. Additionally, we analyze the principles behind the performance enhancement due to false-positive sampling. Our experiments demonstrate that models utilizing false-positive sampling show a reduction in false positives and exhibit improved object detection performance. On the KITTI and Waymo Open datasets, models with false-positive sampling surpass the baseline models by a large margin.<|reference_end|>
@article{oh2024false,
    title={False Positive Sampling-based Data Augmentation for Enhanced 3D Object
  Detection Accuracy},
    author={Jiyong Oh, Junhaeng Lee, Woongchan Byun, Minsang Kong and Sang Hun Lee},
    journal={arXiv preprint arXiv:2403.02639},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02639},
    primaryClass={cs.CV cs.LG}
}