MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model (2311.07198v1)

Abstract: 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.

• The paper formulates self-supervised monocular depth estimation as an iterative denoising process guided by visual conditions using a diffusion model.
• MonoDiffusion trains without ground-truth depth data by employing a pseudo ground-truth diffusion process generated by a teacher model and knowledge distillation.
• The method achieves state-of-the-art results on KITTI and Make3D datasets, demonstrating improved accuracy and zero-shot generalization capabilities.

MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

The paper "MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model" presents a novel approach to monocular depth estimation, a fundamental task in computer vision, which predicts depth from single images for applications like 3D reconstruction and autonomous driving. This method eschews the need for ground-truth depth information during training, leveraging a self-supervised framework and the potential of diffusion models, traditionally used in generative tasks, to enhance depth prediction.

Key Contributions and Methodology

MonoDiffusion introduces the concept of reformulating the monocular depth estimation (MDE) as an iterative denoising process guided by visual conditions:

1. Depth Estimation as Denoising: The authors propose a framework where the depth estimation is approached as a task of iterative denoising. Starting from a random depth distribution, the model refines estimates iteratively, enabled by visual contextual cues, akin to the diffusion model processes used in generative contexts.
2. Pseudo Ground-Truth Diffusion: One of the challenges in adapting diffusion models to self-supervised MDE is the absence of depth ground-truth during training. MonoDiffusion circumvents this by introducing a pseudo ground-truth diffusion process. A pre-trained teacher model generates depth maps that are then progressively refined. This pseudo ground-truth helps train the student model without actual depth data.
3. Masked Visual Condition Mechanism: Inspired by the utility of masked image modeling, the authors incorporate a masked visual condition mechanism to strengthen the denoising capability of their model. By masking parts of the input during training, they force the model to rely on broader contextual information, improving its robustness to occlusions and dynamic elements in scenes.
4. Knowledge Distillation: The framework uses a distillation loss to further refine the model's outputs. This loss helps in guiding the student model using the outputs from the pseudo ground-truth generated by the teacher network, enhancing the learning process and mitigating depth errors.

Experimental Results

The authors evaluate the MonoDiffusion model against state-of-the-art methods using the KITTI and Make3D datasets. The evaluation metrics include both depth error (Abs Rel, Sq Rel, RMSE, RMSE log) and depth accuracy ($\delta < 1.25$, $\delta < 1.25^2$, $\delta < 1.25^3$). MonoDiffusion surpasses previous techniques in these evaluations, demonstrating lower error rates and higher accuracy. Importantly, it also showcases zero-shot generalization capabilities, which is appealing for real-world applications.

Implications and Future Directions

MonoDiffusion's approach to integrating diffusion models in self-supervised monocular depth estimation suggests a fruitful direction for future research by leveraging generative models for tasks beyond perception and image synthesis.

This paper underscores the utility of diffusion models in refining depth estimates, potentially inspiring future applications in other domains of computer vision and artificial intelligence. The combination of self-supervised learning paradigms with masked modeling techniques also highlights pathways for models to achieve notable performance gains without reliance on annotated datasets.

Going forward, further exploration could deepen the efficacy of diffusion processes, tailor step sizes in denoising dynamically, or broaden the pseudo ground-truth generation to encompass more complex and diverse environmental scenes. These advances could refine models' adaptability in dynamically changing real-world contexts, crucial for applications in autonomous systems and real-time 3D environmental mapping.