Depth Anything 3 is a research-driven project that brings accurate and dense depth estimation to any input image or video, enabling foundational understanding of 3D structure from 2D visual content. Designed to work across diverse scenes, lighting conditions, and image types, it uses advanced neural networks trained on large, heterogeneous datasets, producing depth maps that reveal scene depth relationships and object surfaces with strong fidelity. The model can be applied to photography, AR/VR content creation, robotics perception, and 3D reconstruction workflows, making it versatile across industries and research domains. It includes support for high-resolution inputs and post-processing tools that refine depth predictions, helping downstream tasks like segmentation, bounding volume estimation, and mixed reality layering.
Features
- Dense depth estimation from 2D images/video
- High fidelity across diverse content
- Support for high-resolution inputs
- Post-processing refinement tools
- Code for seamless integration into pipelines
- Useful for AR/VR, robotics, and 3D reconstruction