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README.md

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+---
+license: apache-2.0
+pipeline_tag: depth-estimation
+---
+
+# Prompt-Depth-Anything-Vits
+
+## 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-vits").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}
+}