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language:
- en
tags:
- <relevant tags to be included in HF filters>
- facebook
- meta
- pytorch
- DepthLM
base_model:
- facebook/DepthLM
library_name: transformers
---

# Model Details

Official model card of the ICLR2026 oral paper "[DepthLM: Metric Depth from Vision Language Models](https://arxiv.org/pdf/2509.25413)". See our [github](https://github.com/facebookresearch/DepthLM_Official) for the eval and training code.

![image/jpeg](teaser.png)

![image/jpeg](multiTask.jpg)

This model card includes the 12b model of DepthLM finetuned from [Pixtral](https://huggingface.co/mistralai/Pixtral-12B-2409).

We show for the first time that VLMs can achieve comparable accuracy with pure vision models on metric depth estimation, with standard text-based SFT and no architecture chagne, i.e., no dense prediction head or regression/regularization loss is needed. Due to the simplicity, we can use DepthLM to train a unified VLM to handle various complex 3D understanding tasks such as speed or time estimation, and metric scale camera pose estimation, which require different architecture or hand-crafted pipelines in pure vision models.

## Citation

If you find our code useful for your research, please consider citing:

      @article{cai2025depthlm,
          title={DepthLM: Metric Depth from Vision Language Models},
          author={Cai, Zhipeng and Yeh, Ching-Feng and Hu, Xu and Liu, Zhuang and Meyer, Gregory and Lei, Xinjie and Zhao, Changsheng and Li, Shang-Wen and Chandra, Vikas and Shi, Yangyang},
          journal={arXiv preprint arXiv:2509.25413},
          year={2025},
      }

## Contact
Zhipeng Cai, Meta Inc, homepage: https://zhipengcai.github.io/, email: czptc2h at gmail dot com.

## Results

### Comparison with VLMs

| Accuracy ($\delta_1$) | Argoverse2 | DDAD | NuScenes | ETH3D | ScanNet++ | sunRGBD | iBims1 | NYUv2 | avg. |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Qwen2.5-VL (3B) | 0.133 | 0.083 | 0.090 | 0.087 | 0.120 | 0.134 | 0.080 | 0.128 | 0.106 |
| Qwen2.5-VL (7B) | 0.077 | 0.120 | 0.070 | 0.126 | 0.135 | 0.089 | 0.160 | 0.168 | 0.118 |
| Qwen2.5-VL (72B) | 0.119 | 0.140 | 0.186 | 0.220 | 0.272 | 0.276 | 0.212 | 0.324 | 0.219 |
| Seed1.5-VL | 0.009 | 0.012 | 0.013 | 0.219 | 0.495 | 0.321 | 0.459 | 0.412 | 0.243 |
| Gemini-2.5-PRO | 0.280 | 0.252 | 0.365 | 0.328 | 0.380 | 0.270 | 0.466 | 0.394 | 0.342 |
| GPT-5 | 0.218 | 0.302 | 0.382 | 0.313 | 0.428 | 0.471 | 0.307 | 0.540 | 0.370 |
| **Ours (3B)** | 0.808 | 0.724 | **0.870** | **0.745** | 0.838 | 0.850 | 0.890 | 0.868 | 0.824 |
| **Ours (7B)** | **0.833** | **0.747** | 0.865 | 0.718 | **0.850** | **0.859** | **0.920** | **0.915** | **0.838** |
| **Ours - Pixtral (12B)** | 0.734 | 0.670 | 0.819 | 0.653 | 0.834 | 0.786 | 0.870 | 0.799 | 0.771 |


### Comparison with pure vision models

| Accuracy ($\delta_1$) | DDAD | NuScenes | ETH3D | sunRGBD | iBims1 | vs Ours |
| --- | --- | --- | --- | --- | --- | --- |
| ZoeDepth | 0.272 | 0.283 | 0.350 | 0.867 | 0.580 | -42.8% |
| DepthAnything | - | 0.354 | 0.093 | 0.850 | 0.714 | -40.3% |
| DepthAnythingV2 | - | 0.171 | 0.363 | 0.724 | - | -48.5% |
| Metric3D | - | 0.723 | 0.456 | 0.154 | 0.797 | -36.6% |
| Unidepth | 0.858 | 0.846 | 0.185 | 0.943 | 0.157 | -27.3% |
| Depth Pro | 0.299 | 0.566 | 0.397 | 0.831 | 0.823 | -29.1% |
| Metric3Dv2 | - | 0.841 | 0.900 | 0.812 | 0.684 | -3.8% |
| UnidepthV2 | 0.882 | 0.870 | 0.852 | 0.964 | 0.945 | +9.2% |
| **Ours (7B)** | 0.747 | 0.865 | 0.718 | 0.859 | 0.920 | - |

# License

DepthLM is [FAIR NC licensed](https://huggingface.co/facebook/DepthLM/blob/main/LICENSE) as of now