تقرير
SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensors
| العنوان: | SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensors |
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| المؤلفون: | Duarte, Alexandre, Fernandes, Francisco, Pereira, João M., Moreira, Catarina, Nascimento, Jacinto C., Jorge, Joaquim |
| المصدر: | Journal of Real-Time Image Processing 2024 |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction |
| الوصف: | 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. Comment: 13pp, 5 figures, 1 table |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/s11554-024-01491-z |
| URL الوصول: | http://arxiv.org/abs/2406.03388 |
| رقم الأكسشن: | edsarx.2406.03388 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/s11554-024-01491-z |
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