DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis

@article{Yang2022DCLDC,
  title={DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis},
  author={Yanchao Yang and Yuefan Shen and Youyi Zheng and C. Karen Liu and Leonidas J. Guibas},
  journal={IEEE Robotics and Automation Letters},
  year={2022},
  volume={PP},
  pages={1-1}
}
We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic and synthesized depth. The synthesized realistic depth can then be used to train task-specific networks facilitating label transfer from the synthetic domain. Unlike existing image synthesis pipelines, where geometries are mostly ignored, we treat geometries carried by the depth scans based on their own existence. We… 
1 Citations

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