JonnyYu828/DepthVLM-4B · Pirate Face

Unlocking Dense Metric Depth Estimation in VLMs

Project Page: depthvlm.github.io | GitHub: hanxunyu/DepthVLM | arXiv: 2605.15876

📰 News

2026.05 — Released DepthVLM-Bench.
2026.05 — Released DepthVLM-4B.

🌟 Model Overview

DepthVLM serves as a unified foundation model for both low-level dense geometry prediction and high-level multimodal understanding, while achieving substantially faster inference compared with existing VLM-based approaches such as DepthLM and Youtu-VL.

By attaching a lightweight depth head to the LLM backbone and adopting a two-stage supervision paradigm, DepthVLM transforms a single VLM into a native dense geometry predictor, while preserving its multimodal capabilities and enhancing its spatial reasoning.

🧠 Key Characteristics

Native Dense Metric Depth Estimation in VLMs: Directly predicts geometry within the VLM framework.
Unified Multimodal Understanding and Geometry Prediction: Generates full-resolution depth maps alongside language outputs in a single forward pass.
Efficient Inference: Achieves higher efficiency compared to per-pixel query or coarse token-level outputs.
Versatile Application: Supports both indoor and outdoor metric depth estimation.
Improved 3D Spatial Reasoning: Moving toward a truly unified foundation model.

🚀 Main Results

Comparison with VLMs

Benchmark	Ours-8B	Ours-4B	DepthLM-12B	Youtu-VL-4B
Argoverse2	0.798	0.810	0.761	0.663
Waymo	0.865	0.879	0.588	0.473
DDAD	0.813	0.818	0.654	0.342
NuScenes	0.831	0.821	0.736	0.698
ETH3D	0.928	0.924	0.666	0.286
ScanNet++	0.901	0.861	0.756	0.522
SUN RGB-D	0.889	0.882	0.785	0.734
IBims-1	0.936	0.912	0.754	0.856
NYUv2	0.920	0.908	0.866	0.849

Bold and underlined indicate the best and second-best results among the compared models. More details can be found in our paper.

Citation

If you find DepthVLM useful for your research or applications, please consider citing our work using the following BibTeX:

@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}
}

Loading…

🌟 Model Overview

🧠 Key Characteristics

Native Dense Metric Depth Estimation in VLMs: Directly predicts geometry within the VLM framework.

Unified Multimodal Understanding and Geometry Prediction: Generates full-resolution depth maps alongside language outputs in a single forward pass.

Efficient Inference: Achieves higher efficiency compared to per-pixel query or coarse token-level outputs.

Versatile Application: Supports both indoor and outdoor metric depth estimation.

Improved 3D Spatial Reasoning: Moving toward a truly unified foundation model.

🚀 Main Results

Comparison with VLMs

Benchmark	Ours-8B	Ours-4B	DepthLM-12B	Youtu-VL-4B
Argoverse2	0.798	0.810	0.761	0.663
Waymo	0.865	0.879	0.588	0.473
DDAD	0.813	0.818	0.654	0.342
NuScenes	0.831	0.821	0.736	0.698
ETH3D	0.928	0.924	0.666	0.286
ScanNet++	0.901	0.861	0.756	0.522
SUN RGB-D	0.889	0.882	0.785	0.734
IBims-1	0.936	0.912	0.754	0.856
NYUv2	0.920	0.908	0.866	0.849

Bold and underlined indicate the best and second-best results among the compared models. More details can be found in our paper.

Citation

If you find DepthVLM useful for your research or applications, please consider citing our work using the following BibTeX:

@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} }