Depth-Anything-V2 is a computer vision foundation model designed for general-purpose spatial understanding and depth perception. It functions as a monocular depth estimation model that predicts relative and absolute depth maps from single images or video sequences.
The project provides specialized tools for both relative depth estimation and metric depth calculation, allowing for the determination of absolute physical distances in indoor and outdoor environments. It includes a video depth estimation framework that ensures temporal consistency across sequential frames to maintain stable depth predictions.
The system utilizes a multi-scale model hierarchy to balance inference speed and accuracy, extracting global context through a transformer-based encoder. Its capabilities cover spatial scene understanding and the export of predicted depth results as grayscale or colorized images.