Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion

TL;DR

This paper introduces a robust unsupervised monocular depth estimation method using a hierarchical feature-guided diffusion model, improving depth learning and consistency in noisy real-world images.

Contribution

It proposes a novel hierarchical feature-guided denoising module and an implicit depth consistency loss within a diffusion-based framework for depth estimation.

Findings

Outperforms existing generative-based depth estimation models.

Demonstrates high robustness on diverse datasets.

Ensures scale consistency in video sequences.

Abstract

Unsupervised monocular depth estimation has received widespread attention because of its capability to train without ground truth. In real-world scenarios, the images may be blurry or noisy due to the influence of weather conditions and inherent limitations of the camera. Therefore, it is particularly important to develop a robust depth estimation model. Benefiting from the training strategies of generative networks, generative-based methods often exhibit enhanced robustness. In light of this, we employ a well-converging diffusion model among generative networks for unsupervised monocular depth estimation. Additionally, we propose a hierarchical feature-guided denoising module. This model significantly enriches the model's capacity for learning and interpreting depth distribution by fully leveraging image features to guide the denoising process. Furthermore, we explore the implicit depth within reprojection and design an implicit depth consistency loss. This loss function serves to enhance the performance of the model and ensure the scale consistency of depth within a video sequence. We conduct experiments on the KITTI, Make3D, and our self-collected SIMIT datasets. The results indicate that our approach stands out among generative-based models, while also showcasing remarkable robustness.