BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation

@article{Tang2021BridgeNetAJ,
  title={BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation},
  author={Qi Tang and Runmin Cong and Ronghui Sheng and Lingzhi He and Dan Zhang and Yao-Dong Zhao and Sam Tak Wu Kwong},
  journal={Proceedings of the 29th ACM International Conference on Multimedia},
  year={2021}
}
  • Qi Tang, Runmin Cong, +4 authors S. Kwong
  • Published 2021
  • Computer Science
  • Proceedings of the 29th ACM International Conference on Multimedia
Depth map super-resolution is a task with high practical application requirements in the industry. Existing color-guided depth map super-resolution methods usually necessitate an extra branch to extract high-frequency detail information from RGB image to guide the low-resolution depth map reconstruction. However, because there are still some differences between the two modalities, direct information transmission in the feature dimension or edge map dimension cannot achieve satisfactory result… Expand

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