Corpus ID: 236428749

ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation

@article{Wu2021ReDALRA,
  title={ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation},
  author={Tsung-Han Wu and Yueh-Cheng Liu and Yu-Kai Huang and Hsin-Ying Lee and Hung-Ting Su and Ping-Chia Huang and Winston H. Hsu},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.11769}
}
Despite the success of deep learning on supervised point cloud semantic segmentation, obtaining large-scale pointby-point manual annotations is still a significant challenge. To reduce the huge annotation burden, we propose a Region-based and Diversity-aware Active Learning (ReDAL), a general framework for many deep learning approaches, aiming to automatically select only informative and diverse sub-scene regions for label acquisition. Observing that only a small portion of annotated regions… Expand

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