Marigold, a recent diffusion model and associated fine-tuning protocol shows impressive results for monocular depth estimation. Its core principle is to leverage the rich visual knowledge in Stable Diffusion, and fine-tune with a small amount of synthetic data. Although showing very great results, there are many exciting future directions to explore, e.g.: How to speed up? How to adapt it for other modalities? High-resolution? How to predict depth in metrics, etc.
**Requirements**
- Programming in Python and Pytorch
- Good knowledge of deep learning
- [Preferable] have experience in diffusion models
**Reference**
Marigold paper: https://arxiv.org/abs/2312.02145
Marigold, a recent diffusion model and associated fine-tuning protocol shows impressive results for monocular depth estimation. Its core principle is to leverage the rich visual knowledge in Stable Diffusion, and fine-tune with a small amount of synthetic data. Although showing very great results, there are many exciting future directions to explore, e.g.: How to speed up? How to adapt it for other modalities? High-resolution? How to predict depth in metrics, etc.
**Requirements**
- Programming in Python and Pytorch
- Good knowledge of deep learning
- [Preferable] have experience in diffusion models