# Hierarchical Guided-Image-Filtering for Efficient Stereo Matching
## 4 Conclusions  
In this work, we propose a novel stereo matching scheme to make use of hierarchical pattern information in stereo matching. The scheme exploits feature with different level of scales for matching metrics.  
Inspired by the scheme of [PERSON] (2018) for multi-scale disparity cost aggregation, the scheme uses a hierarchy of parameters of the GIF-based linear models and exploits the pervasive  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image Sets** & **FCVF** & **CS-FCVF** & **PGIF** & **CS-PGIF** & **DSG** & **SRDD** & **Proposed** \\ \hline Adirondack & 8.78 & 7.93 & 6.43 & 6.30 & 8.98 & 6.53 & **5.73** \\ ArtL & 12.22 & 12.20 & 10.90 & **10.78** & 13.79 & 13.17 & 10.85 \\ Jadeplant & 21.81 & 22.85 & 20.03 & 20.43 & 21.22 & **18.98** & 19.67 \\ Motorcycle & 9.87 & 9.58 & 9.01 & 8.88 & 8.66 & 9.02 & **8.45** \\ MotorcycleE & 9.72 & 9.27 & 8.93 & 8.60 & 7.78 & **7.69** & 8.47 \\ Piano & 16.20 & 14.65 & 14.01 & **13.53** & 17.55 & 14.88 & 14.02 \\ PianoL & 33.44 & 33.10 & 30.25 & 30.61 & 31.41 & **26.65** & 29.32 \\ Pipes & 10.45 & 10.87 & 9.96 & 10.49 & 12.38 & 11.67 & **9.85** \\ Playroom & 22.68 & 19.41 & 16.62 & 15.63 & 23.98 & 16.25 & **15.18** \\ Playtable & 41.89 & 20.04 & 39.29 & 20.77 & 36.67 & 18.34 & **16.71** \\ PlaytableP & 13.81 & 12.10 & 13.08 & 11.25 & 19.91 & **10.55** & 10.98 \\ Recycle & 10.52 & 10.75 & 8.38 & 9.16 & 11.44 & 9.18 & **8.28** \\ Shelves & 39.52 & 34.52 & 36.11 & 31.92 & 41.13 & 37.88 & **31.63** \\ Teddy & 7.18 & 6.60 & 6.33 & **5.27** & 8.24 & 6.26 & 5.57 \\ Vintage & 33.22 & 29.73 & 30.05 & 29.83 & 33.53 & 27.30 & **26.97** \\ Weighted Average & 16.47 & 14.82 & 14.66 & 13.53 & 17.06 & 13.69 & **12.94** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the weighted average error rates in the all-region between seven algorithms (%). All of the results are obtained without the refinement procedure except SRDD (2019). The lowest error records are marked in bold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Adirondack** & **Playroom** & **Playtable** & **Shelves** \\ \hline FCVF & 15 & 16 & 14 & 15 \\ CS-FCVF & 20 & 21 & 18 & 18 \\ PGIF & 23 & 24 & 22 & 22 \\ CS-PGIF & 28 & 29 & 26 & 26 \\ Proposed & 31 & 31 & 29 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the computation time for four selected image sets between five algorithms (s).  
guided-image-filtering [25] for efficient matching cost calculation. The resultant multi-scale features are collected to form an improved cost volume for disparity estimation by using a linear combination of the guidance image.  
A performance evaluation of version 3 of the Middlebury stereo evaluation data set [31] showed that the proposed solution provided superior disparity accuracy and comparable processing speed when compared with the representative stereo matching algorithms. Besides, the scheme outperformed most of the state-of-art algorithms even without the refinement procedure.  
Conceptualization, C.Z. and Y.-Z.C.; Formal analysis, C.Z. and Y.-Z.C.; Funding acquisition, Y.-Z.C.; Investigation, Y.-Z.C.; Methodology, C.Z.; Project administration, Y.-Z.C.; Supervision, Y.-Z.C.; Writing--original draft, C.Z. and Y.-Z.C.; Writing--review & editing, Y.-Z.C.  
This research was funded by the National Natural Science Foundation of China, under Grant No. 61471263; the Natural Science Foundation of Tianjin, China, under Grant No. 16 JCZDJC31100; the Ministry of Science and Technology, Taiwan, R.O.C., under Grant No. MOST 107-2221-E-182-078 and MOST 108-2221-E-182-061; and Chang Gung Memorial Hospital, Taiwan, under Grant No. CORPD2H0011 and CORPD2J0041.  
The authors declare no conflict of interest.