MonoDiffusion: Self-Supervised Monocular Depth Estimation Using
  Diffusion Model
Shuwei Shao Zhongcai Pei Weihai Chen Dingchi Sun Peter C. Y. Chen Zhengguo Li
Over the past few years, self-supervised monocular depth estimation that does
not depend on ground-truth during the training phase has received widespread
attention. Most efforts focus on designing different types of network
architectures and loss functions or handling edge cases, e.g., occlusion and
dynamic objects. In this work, we introduce a novel self-supervised depth
estimation framework, dubbed MonoDiffusion, by formulating it as an iterative
denoising process. Because the depth ground-truth is unavailable in the
training phase, we develop a pseudo ground-truth diffusion process to assist
the diffusion in MonoDiffusion. The pseudo ground-truth diffusion gradually
adds noise to the depth map generated by a pre-trained teacher model.
Moreover,the teacher model allows applying a distillation loss to guide the
denoised depth. Further, we develop a masked visual condition mechanism to
enhance the denoising ability of model. Extensive experiments are conducted on
the KITTI and Make3D datasets and the proposed MonoDiffusion outperforms prior
state-of-the-art competitors. The source code will be available at
https://github.com/ShuweiShao/MonoDiffusion.