Shao, S., Pei, Z., Chen, W., Sun, D., Chen, P. C. Y., & Li, Z. (2025). MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model. IEEE Transactions on Circuits and Systems for Video Technology, 35(4), 3664–3678. https://doi.org/10.1109/tcsvt.2024.3509619
Abstract:
Over the past few years, self-supervised monocular depth estimation has received widespread attention. Most efforts focus on designing different types of network architectures and loss functions or handling edge cases, for example, occlusion
and dynamic objects. In this work, we take another path and
propose a novel conditional diffusion-based generative framework for self-supervised monocular depth estimation, dubbed MonoDiffusion. Because the depth ground-truth is unavailable in a self-supervised setting, we develop a new pseudo ground-truth diffusion process to assist the diffusion for training. Instead of diffusing at a fixed high resolution, we perform diffusion in a coarse-to-fine manner that allows for faster inference time without sacrificing accuracy or even better accuracy. Furthermore, we develop a simple yet effective contrastive depth reconstruction mechanism to enhance the denoising ability of model. It is worth
noting that the proposed MonoDiffusion has the property of naturally acquiring the depth uncertainty that is essential to be implemented in safety-critical cases. Extensive experiments on the KITTI, Make3D and DIML datasets indicate that our MonoDiffusion outperforms prior state-of-the-art self-supervised competitors. The source code will be publicly available upon the acceptance.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Manufacturing, Trade and Connectivity (MTC) Programmatic Funds
Grant Reference no. : M23L7b0021