Depth Image Completion through Iterative Low-Pass Filtering - Tzu-Kai Wang; Yeh-Wei Yu; Tsung-Hsun Yang; Pin-Duan Huang; Guan-Yu Zhu; Chi-Chung Lau; Ching-Cherng Sun (Applied Sciences, 2024)
This study introduces a spatial-modulated approach designed to recover missing data in in-depth images. Typically, commercial-grade RGB-D cameras utilize structured light or time-of-flight techniques for capturing scene depth. However, these conventional methods encounter difficulties in acquiring depth data from glossy, transparent, or low-reflective surfaces. Additionally, they are prone to interference from broad-spectrum light sources, resulting in defective areas in the captured data. The generation of dense data is further compromised by the influence of noise. In response to these challenges, we implemented an iterative low-pass filter in the frequency domain, effectively mitigating noise and restoring high-quality depth data across all surfaces. To assess the efficacy of our method, deliberate introduction of significant noise and induced defects in the generated depth images was performed. The experimental results unequivocally demonstrate the promising accuracy, precision, and noise-resilient capabilities of our approach. Our implementation is publicly available on the project’s webpage.