SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensors

Abstract: Depth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources. Data-driven denoising algorithms can mitigate such problems. However, they require vast amounts of ground truth depth data. Recent research has tackled this limitation using self-supervised learning techniques, but it requires multiple RGB-D sensors. Moreover, most existing approaches focus on denoising single isolated depth maps or specific subjects of interest, highlighting a need for methods to effectively denoise depth maps in real-time dynamic environments. This paper extends state-of-the-art approaches for depth-denoising commodity depth devices, proposing SelfReDepth, a self-supervised deep learning technique for depth restoration, via denoising and hole-filling by inpainting full-depth maps captured with RGB-D sensors. The algorithm targets depth data in video streams, utilizing multiple sequential depth frames coupled with color data to achieve high-quality depth videos with temporal coherence. Finally, SelfReDepth is designed to be compatible with various RGB-D sensors and usable in real-time scenarios as a pre-processing step before applying other depth-dependent algorithms. Our results demonstrate our approach's real-time performance on real-world datasets. They show that it outperforms state-of-the-art denoising and restoration performance at over 30fps on Commercial Depth Cameras, with potential benefits for augmented and mixed-reality applications.

• The paper introduces a U-Net-inspired autoencoder that learns to restore depth maps without needing ground truth data.
• It achieves real-time processing over 30 FPS by leveraging sequential RGB guidance for temporally coherent denoising.
• Experimental results on CoRBS and InteriorNet confirm that SelfReDepth outperforms existing denoising methods for consumer sensors.

An Expert Overview of "SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensors"

The paper "SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensors" proposes a novel deep learning approach tackling the prevalent issues in depth maps generated by consumer-grade RGB-D sensors. Such devices, including popular models like the Kinect v2, often suffer from noisy and incomplete depth data, which undermines their utility in applications ranging from augmented reality to autonomous navigation.

The Core Proposition

SelfReDepth (SReD) is introduced as an innovative self-supervised learning methodology that efficiently denoises and completes depth maps in real-time. Operating without the necessity for ground-truth data, SReD stands out by leveraging only the noisy input data to learn its denoising and inpainting functions. The backbone of SReD is a convolutional autoencoder inspired by the U-Net architecture, which is adeptly modified to ensure temporal coherence across sequences in dynamic environments.

Key Contributions

The paper delineates four significant contributions of SelfReDepth:

1. Convolutional Autoencoder: A U-Net-inspired architecture is employed to handle sequential frames, maintaining temporal coherence while processing RGB and sequential depth inputs.
2. Real-Time Performance: The method achieves real-time performance at over 30 frames per second, offering practical applications as a pre-processing step for depth data-dependent algorithms.
3. RGB Guidance: RGB data guide the depth restoration process. By utilizing color information, the model enhances inpainting capabilities to manage missing depth values more effectively.
4. Temporal Approach: A video-centric approach ensures temporal consistency in denoised output, addressing limitations of techniques that handle single frames in isolation.

Experimental Validation

Empirical results demonstrate the superiority of SelfReDepth over existing state-of-the-art techniques. The algorithm not only outperforms traditional and learning-based denoisers in terms of noise reduction but also presents greater temporal coherence, quantified using multiple structured and non-reference metrics. The evaluations are conducted on datasets such as CoRBS and the synthetic InteriorNet, where SelfReDepth's effectiveness in both realistic and controlled noisy conditions is confirmed.

Implications and Future Directions

From a theoretical standpoint, SelfReDepth exemplifies advancements in self-supervised noise reduction techniques, validating the feasibility of learning robust models from noisy input data alone. Practically, the model's compatibility with various RGB-D sensors broadens its potential use in diverse applications such as digital content creation, surveillance, and human-computer interaction.

Future developments in the field might focus on refining the preservation of fine image details and enhancing the architecture to address high-frequency temporal noise. Additionally, exploring synthetic training data could further improve depth inpainting performance by providing expansive, controlled datasets for model training. Integrating these advancements could lead to improved adaptability of self-supervised models to more complex, real-world scenarios.

In conclusion, the SelfReDepth framework represents a compelling step toward efficient and versatile depth data restoration, contributing valuable insights to enhance consumer-grade sensor performance in both existing and emerging technological fields.