zoedepth
Maintainer: cjwbw
3.4K
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| Paper Link | View on Arxiv |
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Model overview
The zoedepth model is a novel approach to monocular depth estimation that combines relative and metric depth cues. Developed by researchers at the ISL Organization, it builds on prior work like MiDaS and Depth Anything to achieve state-of-the-art results on benchmarks like NYU Depth v2.
Model inputs and outputs
The zoedepth model takes a single RGB image as input and outputs a depth map. This depth map can be represented as a numpy array, a PIL Image, or a PyTorch tensor, depending on the user's preference. The model supports both high-resolution and low-resolution inputs, making it suitable for a variety of applications.
Inputs
- Image: The input RGB image, which can be provided as a file path, a URL, or a PIL Image object.
Outputs
- Depth Map: The predicted depth map, which can be output as a numpy array, a PIL Image, or a PyTorch tensor.
Capabilities
The zoedepth model's key innovation is its ability to combine relative and metric depth cues to achieve accurate and robust monocular depth estimation. This leads to improved performance on challenging scenarios like unseen environments, low-texture regions, and occlusions, compared to prior methods.
What can I use it for?
The zoedepth model has a wide range of potential applications, including:
- Augmented Reality: The depth maps generated by
zoedepthcan be used to create realistic depth-based effects in AR applications, such as occlusion handling and 3D scene reconstruction. - Robotics and Autonomous Navigation: The model's ability to accurately estimate depth from a single image can be valuable for robot perception tasks, such as obstacle avoidance and path planning.
- 3D Content Creation: The depth maps produced by
zoedepthcan be used as input for 3D modeling and rendering tasks, enabling the creation of more realistic and immersive digital environments.
Things to try
One interesting aspect of the zoedepth model is its ability to generalize to unseen environments through its combination of relative and metric depth cues. This means you can try using the model to estimate depth in a wide variety of scenes, from indoor spaces to outdoor landscapes, and see how it performs. You can also experiment with different input image sizes and resolutions to find the optimal balance between accuracy and computational efficiency for your particular use case.
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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 BEiTL-512 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-anything can 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-anything encoder 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-anything model using datasets like NYUv2 or KITTI to enable metric depth estimation capabilities. Leverage the model as a backbone**: Use the depth-anything encoder as a backbone for downstream high-level perception tasks like semantic segmentation. Integrate with other AI models**: Combine depth-anything with other AI models, such as the ControlNet model, to enable more sophisticated applications.
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