Update README.md

README.md

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 paper:
   - arxiv: 2605.15876
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+---
+
+# DepthVLM Benchmark
+
+DepthVLM Benchmark 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
+- Supports both geometry prediction and spatial reasoning tasks
+- 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](https://github.com/hanxunyu/DepthVLM) for:
+
+- Data preprocessing
+- Training instructions
+- Evaluation scripts
+- Visualization examples
+
+## 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}
+}