Selfredepth

• Published in: Journal of real-time image processing Vol. 21; no. 4; p. 124
• Main Authors: Duarte, Alexandre, Fernandes, Francisco, Pereira, João M, Moreira, Catarina, Nascimento, Jacinto C, Jorge, Joaquim
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer Nature B.V 01.08.2024
• Subjects: Algorithms; Augmented reality; Deep learning; Machine learning; Missing data; Mixed reality; Noise reduction; Real time; Restoration; Self-supervised learning; Sensors; Video data
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-024-01491-z

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 that can 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 of 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 shows that it outperforms state-of-the-art methods in denoising and restoration performance at over 30 fps on Commercial Depth Cameras, with potential benefits for augmented and mixed-reality applications.