MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

Shuwei Shao¹  Zhongcai Pei¹  Weihai Chen¹  Dingchi Sun¹  Peter C. Y. Chen²  Zhengguo Li³ 

¹Beihang University, ²National University of Singapore, ³A*STAR

• Arxiv 2023 •

Abstract

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.

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The source code is comming!