One Look is Enough: A Novel Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation Models on High-Resolution Images (ICCV 2025)

Byeongjun Kwon     Munchurl Kim^†

^†Corresponding author

¹KAIST (Korea Advanced Institute of Science and Technology), South Korea

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This repository is the official PyTorch implementation of "One Look is Enough: A Novel Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation Models on High-Resolution Images". Our proposed method, PRO, achieves state-of-the-art zero-shot depth accuracy on high-resolution datasets with fine-grained details, outperformaing existing depth refinement methods.

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📧 News

• ⚠ The code will be released later
• Jun 26, 2025: "One Look is Enough" is accepted to ICCV 2025
• Mar 28, 2025: This repository is created

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Results

Please visit our project page for more experimental results.

Citation

If the content is useful, please cite our paper:

@misc{kwon2025onelook,
      title={One Look is Enough: A Novel Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation Models on High-Resolution Images}, 
      author={Byeongjun Kwon and Munchurl Kim},
      year={2025},
      eprint={2503.22351},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.22351}, 
}

License

The source codes including the checkpoint can be freely used for research and education only. Any commercial use should get formal permission from the principal investigator (Prof. Munchurl Kim, mkimee@kaist.ac.kr).

Acknowledgement

This repository is built upon FMA-Net and C-DiffSET.