# Depth Image Completion through Iterative Low-Pass Filtering
## 4 Conclusions  
This study introduced a Depth Image Completion Process method employing an Iterative Low-Pass Filter (ILF). Grounded in spatial frequency modulation and guided by RGB information, our approach leverages RGB images to predict depth data. RGB images play a crucial role in providing an initial estimate of the target surface structure. These initial estimations, coupled with frequency domain weights, significantly expedite the iterative process. The depth images underwent multiple low-pass filter iterations, gradually shifting the cut-off frequency from low to high. Filtered depth images outside the defected area were updated with a raw depth matrix in each iteration. The low-pass filter imparted a smoothing effect on the boundaries of the defected area. After a series of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **RMSE (mm)** & **MAE (mm)** & **Rel** \\ \hline NLSPN [24] & 1962.779 & 1935.296 & 0.701 \\ ILF & 300.224 & 173.014 & 0.008 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation on the NYU Depth V2 dataset [39].  
Figure 9: NYU Depth V2 dataset [39] DICP results.  
iterations, the frequency domain variation converged, resulting in the recovery of depth data within the defected area. L1 norm analysis indicated the optimization characteristics of the ILF, making it applicable in depth completion.  
Additionally, a benchmarking analysis comparing the ILF method with the Non-Local Spatial Propagation Network (NLSPN) using the NYU Depth V2 dataset was conducted. Depth errors arise when inconsistency occurs between RGB data and depth information, often stemming from issues such as reflections in the RGB representation, overexposed regions, or scenes with low illumination. Our evaluation consistently revealed that the ILF method outperforms the NLSPN in key metrics such as the Root Mean Square Error, Mean Absolute Error, and Relative Error. This underscores the superior performance of the ILF approach in-depth image completion.  
[PERSON] wrote the paper and mainly developed the techniques used, including the completion algorithm, system setup, and experiments. [PERSON] and [PERSON] participated in the discussion of the study and contributed to the principle of the iterative low-pass filter and the development of the completion algorithm. [PERSON] contributed to the acquisition of depth images. [PERSON] experimentally demonstrated the benchmarks of the advanced completion methods. [PERSON] participated in the development of the completion algorithm. [PERSON] served as the team leader and submitted the paper. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the National Science and Technology Council of Taiwan, grant numbers 111-2218-E-008-004-MBK, 108-2221-E-008-097-MY3, 108-2221-E-008-084-MY3, and 111-2221-E-008-100.  
Not applicable.  
The benchmark datasets we used are available from the NYU Depth dataset V2 website: [[https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)]([https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)). The NLSPN code is available at: [[https://github.com/zzangjinsun/NLSPN_ECCV20](https://github.com/zzangjinsun/NLSPN_ECCV20)]([https://github.com/zzangjinsun/NLSPN_ECCV20](https://github.com/zzangjinsun/NLSPN_ECCV20)). The ILF code and the benchmark result are available at [[https://github.com/Whachudoing/Depth-image-completion-using-iterative-low-pass-filter_Benchmark](https://github.com/Whachudoing/Depth-image-completion-using-iterative-low-pass-filter_Benchmark)]([https://github.com/Whachudoing/Depth-image-completion-using-iterative-low-pass-filter_Benchmark](https://github.com/Whachudoing/Depth-image-completion-using-iterative-low-pass-filter_Benchmark)).  
The authors declare no conflicts of interest.