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

• URL: http://arxiv.org/abs/2406.03388v1
• Date: Wed, 5 Jun 2024 15:38:02 GMT
• Title: SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensors
• Authors: Alexandre Duarte, Francisco Fernandes, João M. Pereira, Catarina Moreira, Jacinto C. Nascimento, Joaquim Jorge,
• Abstract summary: SelfReDepth is a self-supervised deep learning technique for depth restoration. It uses multiple sequential depth frames and color data to achieve high-quality depth videos with temporal coherence. Our results demonstrate our approach's real-time performance on real-world datasets.
• Score: 42.48726526726542
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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