Depth-Anything — Unleashing the Power of Large-Scale Unlabeled Data for Robust Monocular Depth Estimation

Overview

Depth-Anything is a groundbreaking solution for monocular depth estimation that leverages a combination of 1.5M labeled images and over 62M unlabeled images to provide robust and accurate depth estimation. This project stands out for its innovative approach to utilizing large-scale unlabeled data, which significantly enhances the performance of depth estimation models.

Key Features

Relative Depth Estimation: Foundation models capable of providing robust relative depth estimation for any given image.
Metric Depth Estimation: Fine-tuned models with metric depth information from NYUv2 or KITTI for both in-domain and zero-shot metric depth estimation.
Enhanced ControlNet: A re-trained ControlNet based on Depth Anything for more precise image synthesis.

Use Cases

Automated Driving Systems: Utilize Depth-Anything for accurate depth perception to improve navigation and obstacle detection.
Virtual Reality and Augmented Reality: Employ the model for realistic depth rendering to enhance user immersion.
Robotics: Implement the model for environment mapping and navigation in unstructured spaces.

Advantages

Large-Scale Unlabeled Data Utilization: Leverages an extensive dataset for improved model accuracy.
Versatility: Offers both relative and metric depth estimation capabilities.
Community Support: Active community support with regular updates and new features.

Limitations / Considerations

Computational Resources: May require significant computational power for training and inference.
Data Quality: Performance can be affected by the quality and diversity of the training data.

MiDaS: A monocular depth estimation model that serves as a baseline; Depth-Anything improves upon it by using a larger dataset.
Monodepth2: Another approach to monocular depth estimation; differs in its training methodology and dataset.
RAFT-3D: A related project focusing on video depth estimation; Depth-Anything extends its capabilities to still images.

Basic Information

GitHub: Depth-Anything
Stars: 7,772
License: Unknown
Last Commit: 2025-10-08

📊 Project Information

Project Name: Depth-Anything
GitHub URL: https://github.com/LiheYoung/Depth-Anything
Programming Language: Python
⭐ Stars: 7,772
🍴 Forks: 591
📅 Created: 2024-01-22
🔄 Last Updated: 2025-10-08

🏷️ Project Topics

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🎥 Video Tutorials

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Depth-Anything

Project Description