PGSR

Learning Piecewise Planar Representation for RGB Guided Depth Super-Resolution, in IEEE Transactions on Computational Imaging (TCI), 2024. Ruikang Xu, Mingde Yao, Yuanshen Guan, Zhiwei Xiong.

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Datesets

Preparing

• The NYU_v2 dataset can be downloaded from this link.
• The Middlebury dataset can be downloaded from this link.
• The Lu dataset can be downloaded from this link.
• The RGB-D-D dataset can be downloaded from this link.

Partitioning

• Training Set: We taking the first 1000 pairs from the NYU_v2 dataset as the training set and use the same preprocessing as FDSR and DCTNet.
• Test Set: We use the rest 449 pairs from NYU_v2, Middlebury, Lu and RGB-D-D as the testing set.

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Dependencies

• Python 3.8.8, PyTorch 1.8.0, torchvision 0.9.0.
• NumPy 1.24.2, OpenCV 4.7.0, Tensorboardx 2.5.1, kornia, Pillow, Imageio.

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Quick Start

Inference

cd ./src && python test.py

Training

cd ./src && python train.py

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Contact

Any question regarding this work can be addressed to xurk@mail.ustc.edu.cn.

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Citation

If you find our work helpful, please cite the following paper.

@article{xu2024learning,
  title={Learning Piecewise Planar Representation for RGB Guided Depth Super-Resolution},
  author={Xu, Ruikang and Yao, Mingde and Guan, Yuanshen and Xiong, Zhiwei},
  journal={IEEE Transactions on Computational Imaging},
  year={2024},
  publisher={IEEE}
}