Depth Anything: A Brand-New Approach In Understanding Depth

Depth estimation is a fundamental task in computer vision that has many applications, such as robotics, autonomous driving, and augmented reality. Traditional methods for depth estimation rely on stereo cameras or LiDAR sensors, which can be expensive and bulky.

In recent years, there has been growing interest in monocular depth estimation, which uses only a single RGB camera to estimate depth.

What is Depth Anything?

Depth Anything is a new foundation model for monocular depth estimation that was recently introduced by Lihe Zhang et al. Depth Anything is a convolutional neural network (CNN) that is trained on a combination of labeled and unlabeled data.

• Labeled data consists of images and their corresponding depth maps.
• Unlabeled data consists of images without depth maps.

Depth Anything uses a self-supervised learning approach to train on unlabeled data. Self-supervised learning is a type of machine learning where the model learns from the data itself, without the need for human-labeled data. In the case of Depth Anything, the model learns to predict the depth of an image by reconstructing it from its corresponding depth map.

Depth Anything is available in different model sizes and can be easily integrated into other projects. The smallest model is only 5MB, which makes it suitable for use on mobile devices. The largest model is 150MB, which provides the best accuracy.

Check out the full whitepaper here.

Depth anything on images

The researchers retrained a depth-conditioned ControlNet based on our Depth Anything, better than the previous one based on MiDaS.

Depth anything on videos

Depth Anything is an image-based depth estimation method, but it can also be applied to input videos. Check out how Depth Anything compares to the previous best model.

Depth anything for editing videos

The researchers also showcases how Depth Anything can be utilized to perform video editing. The results look amazing!

Practical Applications

Depth estimation has many practical applications, including:

• Robotics: Robots can use depth estimation to navigate their environment and avoid obstacles.
• Autonomous driving: Self-driving cars can use depth estimation to understand the 3D structure of their surroundings and make safe driving decisions.
• Augmented reality: Augmented reality applications can use depth estimation to overlay digital information onto the real world in a realistic way.

Why Should You Care?

Depth estimation is a fundamental task in computer vision with many potential applications. Depth Anything is a new foundation model that achieves state-of-the-art results on several metrics. It is also efficient and easy to use, which makes it a valuable tool for researchers and developers.

Possible Implications to the World

Depth estimation has the potential to revolutionize many industries. For example, it could lead to the development of safer and more efficient robots, self-driving cars, and augmented reality applications. It could also be used to improve the accuracy of other computer vision tasks, such as object detection and tracking.

Overall, Depth Anything is a promising new foundation model that has the potential to make a significant impact on the world.

Demo

You can try the free demo on HuggingFace.