https://github.com/DepthAnything/PromptDA
[CVPR 2025] Prompt Depth Anything
https://github.com/DepthAnything/PromptDA
3d-reconstruction 4d-reconstruction depth-estimation robotics-grasping
Last synced: about 1 year ago
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[CVPR 2025] Prompt Depth Anything
- Host: GitHub
- URL: https://github.com/DepthAnything/PromptDA
- Owner: DepthAnything
- License: apache-2.0
- Created: 2024-12-17T06:56:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-04T15:13:26.000Z (about 1 year ago)
- Last Synced: 2025-03-04T16:26:28.051Z (about 1 year ago)
- Topics: 3d-reconstruction, 4d-reconstruction, depth-estimation, robotics-grasping
- Language: Python
- Homepage: https://promptda.github.io
- Size: 31.5 MB
- Stars: 586
- Watchers: 12
- Forks: 32
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
### [Project Page](https://promptda.github.io/) | [Paper](https://promptda.github.io/assets/main_paper_with_supp.pdf) | [Hugging Face Demo](https://huggingface.co/spaces/depth-anything/PromptDA) | [Interactive Results](https://promptda.github.io/interactive.html) | [Data](https://promptda.github.io/)
> Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
> [Haotong Lin](https://haotongl.github.io/),
[Sida Peng](https://pengsida.net/),
[Jingxiao Chen](https://scholar.google.com/citations?user=-zs1V28AAAAJ),
[Songyou Peng](https://pengsongyou.github.io/),
[Jiaming Sun](https://jiamingsun.me/),
[Minghuan Liu](https://minghuanliu.com/),
[Hujun Bao](http://www.cad.zju.edu.cn/home/bao/),
[Jiashi Feng](https://scholar.google.com/citations?user=Q8iay0gAAAAJ),
[Xiaowei Zhou](https://www.xzhou.me/),
[Bingyi Kang](https://bingykang.github.io/)
> CVPR 2025
## 🛠️ Installation
Setting up the environment
```bash
git clone https://github.com/DepthAnything/PromptDA.git
cd PromptDA
pip install -r requirements.txt
pip install -e .
sudo apt install ffmpeg # for video generation
```
Pre-trained Models
| Model | Params | Checkpoint |
|:-|-:|:-:|
| Prompt-Depth-Anything-Large | 340M | [Download](https://huggingface.co/depth-anything/prompt-depth-anything-vitl/resolve/main/model.ckpt) |
| Prompt-Depth-Anything-Small | 25.1M | [Download](https://huggingface.co/depth-anything/prompt-depth-anything-vits/resolve/main/model.ckpt) |
| Prompt-Depth-Anything-Small-Transparent | 25.1M | [Download](https://huggingface.co/depth-anything/prompt-depth-anything-vits-transparent/resolve/main/model.ckpt) |
Only Prompt-Depth-Anything-Large is used to benchmark in our paper. Prompt-Depth-Anything-Small-Transparent is further fine-tuned 10K steps with [hammer dataset](https://github.com/Junggy/HAMMER-dataset) with our iPhone lidar simulation method to improve the performance on transparent objects.
## 🚀 Usage
Example 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)
```
## 📸 Running on your own capture
You can use [Stray Scanner App](https://apps.apple.com/us/app/stray-scanner/id1557051662) to capture your own data, which requires iPhone 12 Pro or later Pro models, iPad 2020 Pro or later Pro models. We setup a [Hugging Face Space](https://huggingface.co/spaces/depth-anything/PromptDA) for you to quickly test our model. If you want to obtain video results, please follow the following steps.
Testing steps
1. Capture a scene with the Stray Scanner App. (The charging port is preferred to face downward or to the right.)
2. Use the iPhone Files App to compress it into a zip file and transfer it to your computer. Here is an [example screen recording](https://haotongl.github.io/promptda/assets/ScreenRecording_12-16-2024.mp4).
3. Run the following commands to infer our model and generate the video results.
```bash
export PATH_TO_ZIP_FILE=data/8b98276b0a.zip # Replace with your own zip file path
export PATH_TO_SAVE_FOLDER=data/8b98276b0a_results # Replace with your own save folder path
python3 -m promptda.scripts.infer_stray_scan --input_path ${PATH_TO_ZIP_FILE} --output_path ${PATH_TO_SAVE_FOLDER}
python3 -m promptda.scripts.generate_video process_stray_scan --input_path ${PATH_TO_ZIP_FILE} --result_path ${PATH_TO_SAVE_FOLDER}
ffmpeg -framerate 60 -i ${PATH_TO_SAVE_FOLDER}/%06d_smooth.jpg -c:v libx264 -pix_fmt yuv420p ${PATH_TO_SAVE_FOLDER}.mp4
```
## 👏 Acknowledgements
We thank the generous support from Prof. [Weinan Zhang](https://wnzhang.net/) for robot experiments, including the space, objects and the Unitree H1 robot. We also thank [Zhengbang Zhu](https://scholar.google.com/citations?user=ozatRA0AAAAJ), Jiahang Cao, Xinyao Li, Wentao Dong for their help in setting up the robot platform and collecting robot data.
## 📚 Citation
If you find this code useful for your research, please use the following BibTeX entry
```
@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}
}
```