Self-supervised Indoor Monocular Depth Estimation Framework: F2Depth

Core Concepts

F2Depth proposes a self-supervised indoor monocular depth estimation framework using optical flow consistency and feature map synthesis.

Abstract

• F2Depth addresses the challenge of low-textured indoor scenes in self-supervised depth estimation.
• The framework utilizes optical flow for depth learning and introduces patch-based photometric loss.
• Multi-scale feature map synthesis and optical flow consistency losses enhance depth estimation.
• Experimental results show F2Depth's effectiveness on NYU Depth V2 and Campus Indoor datasets.
• Generalization experiments on 7-Scenes demonstrate F2Depth's superior performance.
• Ablation studies confirm the importance of proposed losses in improving depth estimation.

Stats

"Experimental results on the NYU Depth V2 dataset demonstrate the effectiveness of the framework."
"Zero-shot generalization experiments on 7-Scenes dataset and Campus Indoor achieve δ1 accuracy of 75.8% and 76.0% respectively."

Quotes

"A self-supervised optical flow estimation network is introduced to supervise depth learning."
"Experimental results on the NYU Depth V2 dataset demonstrate the effectiveness of the framework."

Key Insights Distilled From

$\mathrm{F^2Depth}$

by Xiaotong Guo... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18443.pdf

Deeper Inquiries

How does F2Depth compare to other state-of-the-art methods in monocular depth estimation

F2Depth outperforms other state-of-the-art methods in monocular depth estimation, especially in challenging indoor scenes with low-textured regions. Compared to methods like P2Net and Moving Indoor, F2Depth introduces innovative techniques such as optical flow consistency loss and multi-scale feature map synthesis loss. These additions improve the accuracy of depth estimation, particularly in areas with low texture where traditional methods struggle. The results on datasets like NYU Depth V2 and 7-Scenes demonstrate the effectiveness and generalization ability of F2Depth, showcasing its superiority over existing methods.

What are the potential limitations of using optical flow for depth supervision in indoor scenes

While using optical flow for depth supervision in indoor scenes can be beneficial for improving accuracy, there are potential limitations to consider. One limitation is the sensitivity of optical flow estimation to low-textured areas. In indoor environments where walls, floors, and other surfaces lack distinctive features, optical flow may struggle to provide accurate pixel matching pairs. This can lead to errors in depth estimation, especially in regions with uniform textures. Additionally, the computational complexity of optical flow estimation networks can be a limitation, requiring significant resources for training and inference in real-time applications.

How can the findings of F2Depth be applied to real-world applications beyond academic datasets

The findings of F2Depth can have practical applications beyond academic datasets in various real-world scenarios. For instance, in autonomous driving systems, accurate depth estimation is crucial for obstacle detection and navigation. By leveraging the techniques introduced in F2Depth, such systems can better perceive the surrounding environment, especially in indoor settings with complex structures and low-textured areas. Additionally, applications in robotics, augmented reality, and surveillance systems can benefit from improved depth estimation accuracy for scene understanding and interaction with the environment. The robust generalization ability of F2Depth makes it suitable for deployment in diverse indoor environments, enhancing the performance of depth-related tasks in real-world applications.