| --- |
| license: apache-2.0 |
|
|
| task_categories: |
| - depth-estimation |
|
|
| tags: |
| - depth-estimation |
| - 3d-vision |
| - multimodal |
| - metric-depth |
|
|
| paper: |
| - arxiv: 2605.15876 |
| --- |
| |
| # DepthVLM-Bench |
|
|
| DepthVLM-Bench is a unified indoor-outdoor metric depth estimation benchmark designed for vision-language models (VLMs). The benchmark provides diverse indoor and outdoor scenes with metric depth annotations in a unified VLM-compatible format, enabling large multimodal models to jointly learn dense geometry prediction and multimodal understanding. |
|
|
| ## Features |
|
|
| - Unified indoor and outdoor metric depth estimation |
| - VLM-compatible data format |
| - Dense depth supervision for multimodal foundation models |
| - Designed for scalable multimodal training |
|
|
| ## Paper |
|
|
| [Unlocking Dense Metric Depth Estimation in VLMs](https://arxiv.org/abs/2605.15876) |
|
|
| ## Usage |
|
|
| Please refer to the official repository for: |
|
|
| - Data preprocessing |
| - Evaluation scripts |
| - Visualization examples |
|
|
| Repository: https://github.com/hanxunyu/DepthVLM |
|
|
| ## Citation |
|
|
| ```bibtex id="83r6sk" |
| @article{yu2026unlocking, |
| title={Unlocking Dense Metric Depth Estimation in VLMs}, |
| author={Hanxun Yu and Xuan Qu and Yuxin Wang and Jianke Zhu and Lei Ke}, |
| journal={arXiv preprint arXiv:2605.15876}, |
| year={2026} |
| } |
