midas
Maintainer: cjwbw
78
| Property | Value |
|---|---|
| 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
midas is a robust monocular depth estimation model developed by researchers at the Intelligent Systems Lab (ISL) at ETH Zurich. It was trained on up to 12 diverse datasets, including ReDWeb, DIML, Movies, MegaDepth, and KITTI, using a multi-objective optimization approach. The model produces high-quality depth maps from a single input image, with several variants offering different trade-offs between accuracy, speed, and model size. This versatility makes midas a practical solution for a wide range of depth estimation applications.
Compared to similar depth estimation models like depth-anything, marigold, and t2i-adapter-sdxl-depth-midas, midas stands out for its robust performance across diverse datasets and its efficient model variants suitable for embedded devices and real-time applications.
Model inputs and outputs
midas takes a single input image and outputs a depth map of the same size, where each pixel value represents the estimated depth at that location. The input image can be of varying resolutions, with the model automatically resizing it to the appropriate size for the selected variant.
Inputs
- Image: The input image for which the depth map should be estimated.
Outputs
- Depth map: The estimated depth map of the input image, where each pixel value represents the depth at that location.
Capabilities
midas is capable of producing high-quality depth maps from a single input image, even in challenging scenes with varying lighting, textures, and objects. The model's robustness is achieved through training on a diverse set of datasets, which allows it to generalize well to unseen environments.
The available model variants offer different trade-offs between accuracy, speed, and model size, making midas suitable for a wide range of applications, from high-quality depth estimation on powerful GPUs to real-time depth sensing on embedded devices.
What can I use it for?
midas can be used in a variety of applications that require robust monocular depth estimation, such as:
- Augmented Reality (AR): Accurate depth information can be used to enable realistic occlusion, lighting, and interaction effects in AR applications.
- Robotics and Autonomous Vehicles: Depth maps can provide valuable input for tasks like obstacle avoidance, navigation, and scene understanding.
- Computational Photography: Depth information can be used to enable advanced features like portrait mode, depth-of-field editing, and 3D photography.
- 3D Reconstruction: Depth maps can be used as a starting point for 3D scene reconstruction from single images.
The maintainer, cjwbw, has also developed other impressive AI models like real-esrgan and supir, showcasing their expertise in computer vision and image processing.
Things to try
One interesting aspect of midas is its ability to handle a wide range of input resolutions, from 224x224 to 512x512, with different model variants optimized for different use cases. You can experiment with different input resolutions and model variants to find the best trade-off between accuracy and inference speed for your specific application.
Additionally, you can explore the model's performance on various datasets and scenarios, such as challenging outdoor environments, low-light conditions, or scenes with complex geometry. This can help you understand the model's strengths and limitations and inform your use cases.
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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