README.md

---

license: apache-2.0
pipeline_tag: depth-estimation
---


# Prompt-Depth-Anything-Vitl

## Introduction

Prompt Depth Anything is a high-resolution and accurate metric depth estimation method, with the following highlights:
- using prompting to unleash the power of depth foundation models, inspired by success of prompting in VLM and LLM foundation models.
- The widely available iPhone LiDAR is taken as the prompt, guiding the model to produce up to 4K resolution accurate metric depth.
- A scalable data pipeline is introduced to train the method.
- Prompt Depth Anything benefits downstream applications, including 3D reconstruction and generalized robotic grasping.

## Installation

```bash

git clone https://github.com/DepthAnything/PromptDA.git

cd PromptDA

pip install -r requirements.txt

pip install -e .

```

## Usage

```python

from promptda.promptda import PromptDA

from promptda.utils.io_wrapper import load_image, load_depth, save_depth



DEVICE = 'cuda'

image_path = "assets/example_images/image.jpg"

prompt_depth_path = "assets/example_images/arkit_depth.png"

image = load_image(image_path).to(DEVICE)

prompt_depth = load_depth(prompt_depth_path).to(DEVICE) # 192x256, ARKit LiDAR depth in meters



model = PromptDA.from_pretrained("depth-anything/prompt-depth-anything-vitl").to(DEVICE).eval()

depth = model.predict(image, prompt_depth) # HxW, depth in meters



save_depth(depth, prompt_depth=prompt_depth, image=image)

```

## Citation

If you find this project useful, please consider citing:

```bibtex

@inproceedings{lin2024promptda,

  title={Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation},

  author={Lin, Haotong and Peng, Sida and Chen, Jingxiao and Peng, Songyou and Sun, Jiaming and Liu, Minghuan and Bao, Hujun and Feng, Jiashi and Zhou, Xiaowei and Kang, Bingyi},

  journal={arXiv},

  year={2024}

}