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---
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license: apache-2.0
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pipeline_tag: depth-estimation
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---
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# Prompt-Depth-Anything-Vitl
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## Introduction
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Prompt Depth Anything is a high-resolution and accurate metric depth estimation method, with the following highlights:
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- using prompting to unleash the power of depth foundation models, inspired by success of prompting in VLM and LLM foundation models.
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- The widely available iPhone LiDAR is taken as the prompt, guiding the model to produce up to 4K resolution accurate metric depth.
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- A scalable data pipeline is introduced to train the method.
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- Prompt Depth Anything benefits downstream applications, including 3D reconstruction and generalized robotic grasping.
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## Installation
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```bash
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git clone https://github.com/DepthAnything/PromptDA.git
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cd PromptDA
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pip install -r requirements.txt
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pip install -e .
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```
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## Usage
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```python
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from promptda.promptda import PromptDA
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from promptda.utils.io_wrapper import load_image, load_depth, save_depth
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DEVICE = 'cuda'
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image_path = "assets/example_images/image.jpg"
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prompt_depth_path = "assets/example_images/arkit_depth.png"
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image = load_image(image_path).to(DEVICE)
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prompt_depth = load_depth(prompt_depth_path).to(DEVICE) # 192x256, ARKit LiDAR depth in meters
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model = PromptDA.from_pretrained("depth-anything/prompt-depth-anything-vitl").to(DEVICE).eval()
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depth = model.predict(image, prompt_depth) # HxW, depth in meters
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save_depth(depth, prompt_depth=prompt_depth, image=image)
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```
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## Citation
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If you find this project useful, please consider citing:
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```bibtex
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@inproceedings{lin2024promptda,
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title={Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation},
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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},
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journal={arXiv},
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year={2024}
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} |
