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| | - DepthLM |
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| | 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. |
| |
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| |  |
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| |  |
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|
| | 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 |
