#monocular depth estimation

Depth Anything employs large-scale datasets comprising over 63.5 million images to improve monocular depth estimation. Recognized by CVPR 2024, this method enhances depth prediction capabilities in relative and metric contexts. The project integrates features such as an optimized depth-conditioned ControlNet and supports scene understanding. With the release of Depth Anything V2 and its integration with platforms like Hugging Face, the project offers accessible tools for enhancing depth perception technologies.

Discover the application of diffusion-based image generators in advancing monocular depth estimation, utilizing generative model insights. This project excels in zero-shot transfer and integrates seamlessly with platforms such as Hugging Face and Google Colab, delivering leading-edge results in visual computing.