# Depth Image Completion through Iterative Low-Pass Filtering
## 1 Introduction  
Depth image completion has been a prominent focus in computer vision projects over the past few years, with significant advancements arising from both hardware improvements and developments in image processing. Hardware enhancements aim to achieve precise data detection, real-time data processing, and high filling rates for single frames. Consequently, technologies such as CMOS, compact laser diodes, projection units, and Diffractive Optical Elements (DOEs) have been developed and applied to depth sensors. Commercial-grade RGB-D cameras, including the Microsoft Kinect series, Intel RealSense, LUCID Helios, and Matterport 3D cameras, have found wide-ranging applications in scene reconstruction [1, 2], robotic vision [3, 4, 5], and automotive systems [6, 7]. These devices incorporate advanced optical technologies such as backside-illuminated CMOS [8] and current-assisted photonic demodulators [9], representing a significant leap in light sensitivity, gate-level signal processing, and overall performance. However, despite these technological strides, challenges persist, particularly in dealing with reflective targets, absorbing surfaces, and distant objects [10, 11]. These issues stem from various factors affecting the light conditions, such as strong interference from infrared sources, reflective or transparent surfaces, and highly absorbing materials causing low contrast in infrared images or low Signal-to-noise ratio (SNR). Imaging geometry is an additional factor, causing shadows to be projected due to occlusion objects or the light source's position, resulting in blank areas or shadows in depth images. The pursuit of generating dense and accurate depth information faces many hurdles, with noise emerging as a formidable issue. Interference from broad-spectrum light sources introduces defects and noise, compromising the integrity of captured data. These disruptions can directly impede the structured light patterns and Time-of-Flight (ToF) signals to someextent, resulting in inevitable defects in depth images and the generation of noisy scanning results [12]. This study aims to restore depth images derived from various 3D sensing techniques and accurately locate points in space, especially in areas affected by defects. Several methods have been explored for depth image completion, including extrapolation [13], LiDAR data inpainting with depth gradients [14; 15], and surface reconstruction based on 3D structures [16; 17; 18]. In recent years, depth image completion using deep neural networks has received considerable attention, with representative research in monocular depth estimation [19; 20; 21; 22; 23], RGB-guided methods [24], learning-based surface normal-guided completion [25], and global and local depth prediction with RGB guidance [26]. While these neural network-based methods have achieved significant success in full-depth image processing with large dataset training, profound network structures, and fine-tuned parameters, they often suffer from the loss of fine details and malfunctions in the presence of noisy data [19; 27]. In the realm of advanced depth completion techniques, RGB-guided methods such as Spatial Propagation Networks (SPN) have shown promise in capturing local affinities, resulting in improved depth completion scores. The Non-Local Spatial Propagation Network for Depth Completion (NLSPN) has emerged as a top performer in this regard [24; 28; 29; 30]. Nevertheless, these approaches yield inaccurate results when confronted with glossy surfaces, absorbent surfaces, and noisy depth data. Depth errors occur when RGB data is incoherent with respect to depth data; for example, when the RGB reflects only part of the scene, overexposed areas, or dark scenes. Current advanced methods in depth estimation and completion achieve high scores primarily based on large dataset training, fine-tuning parameters, and the synthetic architecture of networks.