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
this https URL

通过将其作为迭代去噪过程进行建模，我们引入了一种新的自监督深度估计框架 MonoDiffusion，该模型通过伪真实扩散过程生成伪真实数据以辅助扩散，从而解决了缺乏真实深度数据的问题。通过应用蒸馏损失来指导去噪深度，并使用掩码视觉条件机制增强模型的去噪能力。在 KITTI 和 Make3D 数据集上进行的广泛实验证明了 MonoDiffusion 比现有的最先进竞争对手表现更好。