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UniDepthV2: Universal Monocular Metric Depth Estimation

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Project Overview

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UniDepthV2 was released by Luigi Piccinelli et al. in February 2025. UniDepthV2 is able to reconstruct metric 3D scenes from only a single image across domains. Unlike the existing MMDE paradigm, UniDepthV2 directly predicts metric 3D points from the input image at inference time without any additional information, striving to achieve a general and flexible MMDE solution. The related paper results are "UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler".

This tutorial uses resources for a single RTX 4090 card.

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Citation Information

The citation information for this project is as follows:

@inproceedings{piccinelli2024unidepth,
    title     = { {U}ni{D}epth: Universal Monocular Metric Depth Estimation},
    author    = {Piccinelli, Luigi and Yang, Yung-Hsu and Sakaridis, Christos and Segu, Mattia and Li, Siyuan and Van Gool, Luc and Yu, Fisher},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2024}
}

@misc{piccinelli2025unidepthv2,
      title={ {U}ni{D}epth{V2}: Universal Monocular Metric Depth Estimation Made Simpler}, 
      author={Luigi Piccinelli and Christos Sakaridis and Yung-Hsu Yang and Mattia Segu and Siyuan Li and Wim Abbeloos and Luc Van Gool},
      year={2025},
      eprint={2502.20110},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.20110}, 
}