Introduction to Depth Anything
Depth Anything is an innovative project that offers a practical solution for monocular depth estimation, using both labeled and unlabeled images to improve robustness and accuracy. Announced at the Conference on Computer Vision and Pattern Recognition 2024 (CVPR), this project has made significant advancements in the field by employing a vast dataset comprising 1.5 million labeled images and over 62 million unlabeled images.
Core Features of Depth Anything
Relative Depth Estimation
One of the key features of Depth Anything is its ability to provide reliable relative depth estimation for any given image. This versatility makes it an invaluable tool in various applications, offering users robust depth predictions.
Metric Depth Estimation
Depth Anything has demonstrated strong performance in both in-domain and zero-shot metric depth estimation. This is achieved by fine-tuning the model with metric depth data from well-known datasets like NYUv2 or KITTI.
Enhanced Depth-Conditioned ControlNet
The project has integrated an improved version of the depth-conditioned ControlNet, which was re-trained based on Depth Anything’s framework. This new version provides more precise results than its predecessors, making it a preferred choice in various control applications.
Support for High-level Scene Understanding
Beyond depth estimation, Depth Anything’s encoder can be repurposed for high-level perception tasks such as semantic segmentation. The model has shown impressive results on datasets like Cityscapes and ADE20K, expanding its utility to a broader range of computer vision applications.
Performance Comparisons
Depth Anything models outperform previous benchmarks like the MiDaS v3.1 BEiTL-512. The project provides several pre-trained models, each varying in scale and performance. For instance, the large-scale model (Depth-Anything-Large) offers superior performance metrics, excelling in mean absolute error reduction (AbsRel) and the δ metrics, signifying higher prediction accuracy and reliability.
Models and Deployment
The project offers three pre-trained models - small, base, and large. These are designed for scalable depth estimation and can be deployed efficiently on modern GPUs, demonstrating fast inference times on hardware like the NVIDIA V100, A100, and RTX4090.
Getting Started with Depth Anything
Installation and Running
Users can quickly set up the Depth Anything project by cloning the repository and installing required packages. The model can be executed with a simple command that specifies the encoder type, image path, and output directory, among other options. For video applications, the project also supports video depth visualization.
Integration and Flexibility
Developers can easily integrate Depth Anything into their projects. The models can be utilized through platforms like Hugging Face’s Transformers library, enabling quick deployment with minimal code requirements. The project also supports usage in various frameworks such as TensorRT and ONNX for enhanced deployment flexibility.
Community and Support
Depth Anything’s development and deployment have been significantly supported by contributions from the community and partnerships with platforms like Hugging Face. Numerous extensions and integrations have been built around the project, showcasing its adaptability and the enthusiastic backing from the computer vision community.
Acknowledgement and Citation
Depth Anything acknowledges the technical and demo-building support received from the Hugging Face team. The project also highlights its collaborations with other research teams for further evaluations and advancements.
Should you find the project useful, contributions can be cited using the provided citation format from the developers. Depth Anything stands as a testament to the power of leveraging both labeled and unlabeled data for enhanced depth estimation capabilities in computer vision.