Prompt Depth Anything Vitl

--- 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} }

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