depth-anything
Maintainer: cjwbw
3
| Property | Value |
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| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
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Model overview
depth-anything is a highly practical solution for robust monocular depth estimation developed by researchers from The University of Hong Kong, TikTok, Zhejiang Lab, and Zhejiang University. It is trained on a combination of 1.5M labeled images and 62M+ unlabeled images, resulting in strong capabilities for both relative and metric depth estimation. The model outperforms the previously best-performing MiDaS v3.1 BEiT<sub>L-512</sub> model across a range of benchmarks including KITTI, NYUv2, Sintel, DDAD, ETH3D, and DIODE.
The maintainer of depth-anything, cjwbw, has also developed several similar models, including supir, supir-v0f, supir-v0q, and rmgb, which cover a range of image restoration and background removal tasks.
Model inputs and outputs
depth-anything takes a single image as input and outputs a depth map that estimates the relative depth of the scene. The model supports three different encoder architectures - ViTS, ViTB, and ViTL - allowing users to choose the appropriate model size and performance trade-off for their specific use case.
Inputs
- Image: The input image for which depth estimation is to be performed.
- Encoder: The encoder architecture to use, with options of ViTS, ViTB, and ViTL.
Outputs
- Depth map: A depth map that estimates the relative depth of the scene.
Capabilities
depth-anything has shown strong performance on a variety of depth estimation benchmarks, outperforming the previous state-of-the-art MiDaS model. It offers robust relative depth estimation and the ability to fine-tune for metric depth estimation using datasets like NYUv2 and KITTI. The model can also be used as a backbone for downstream high-level scene understanding tasks, such as semantic segmentation.
What can I use it for?
depth-anything can be used for a variety of applications that require accurate depth estimation, such as:
- Robotics and autonomous navigation: The depth maps generated by
depth-anythingcan be used for obstacle detection, path planning, and scene understanding in robotic and autonomous vehicle applications. - Augmented reality and virtual reality: Depth information is crucial for realistic depth-based rendering and occlusion handling in AR/VR applications.
- Computational photography: Depth maps can be used for tasks like portrait mode, bokeh effects, and 3D scene reconstruction in computational photography.
- Scene understanding: The
depth-anythingencoder can be fine-tuned for downstream high-level perception tasks like semantic segmentation, further expanding its utility.
Things to try
With the provided pre-trained models and the flexibility to fine-tune the model for specific use cases, there are many interesting things you can try with depth-anything:
- Explore the different encoder models: Try the ViTS, ViTB, and ViTL encoder models to find the best trade-off between model size, inference speed, and depth estimation accuracy for your application.
- Experiment with metric depth estimation: Fine-tune the
depth-anythingmodel using datasets like NYUv2 or KITTI to enable metric depth estimation capabilities. - Leverage the model as a backbone: Use the
depth-anythingencoder as a backbone for downstream high-level perception tasks like semantic segmentation. - Integrate with other AI models: Combine
depth-anythingwith other AI models, such as the ControlNet model, to enable more sophisticated applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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