Self-supervised Transparent Liquid Segmentation for Robotic Pouring

@article{Narasimhan2022SelfsupervisedTL,
  title={Self-supervised Transparent Liquid Segmentation for Robotic Pouring},
  author={Gautham Narayan Narasimhan and Kai Zhang and Ben Eisner and Xingyu Lin and David Held},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.01538}
}
Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem. We propose a novel segmentation pipeline that can segment transparent liquids such as water from a static, RGB image without requiring any manual annotations or heating of the liquid for training. Instead, we use a generative model that is capable of translating images of colored liquids into synthetically generated transparent liquid images… 

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