arxiv preprint - Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

In this episode we discuss Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation by Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler. The paper presents Marigold, a new method for monocular depth estimation that utilizes the learned priors from generative diffusion models, specifically derived from Stable Diffusion. Marigold is affine-invariant and can be fine-tuned efficiently on synthetic data with a single GPU, offering significant performance improvements, including over 20% gains in certain datasets. The project demonstrates the potential of leveraging the capabilities of generative models for enhancing depth estimation tasks, with a focus on better generalization and state-of-the-art results.