Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation

arXiv:2403.05056v1 Announce Type: new
Abstract: Monocular depth estimation is a crucial task in computer vision. While existing methods have shown impressive results under standard conditions, they often face challenges in reliably performing in scenarios such as low-light or rainy conditions due to the absence of diverse training data. This paper introduces a novel approach named Stealing Stable Diffusion (SSD) prior for robust monocular depth estimation. The approach addresses this limitation by utilizing stable diffusion to generate synthetic images that mimic …

arxiv cs.cv diffusion prior robust stable diffusion stealing type