# Simplified High-Performance Cost Aggregation for Stereo Matching
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Test Sets** & **RTSMNe** & **ISM** & **DDL** & **TPAO** & **IGF** & **ACR-GIF-OW** & **HGIF** & **FASW** & **Proposed** \\ \hline Adiromdack & **4.5** & 15.5 & 6.5 & 7.1 & 14.9 & 7.9 & 5.7 & 6.8 & 4.8 \\ ArtL & 11.5 & 16.5 & 13.1 & 13.2 & 15.9 & 10.1 & 10.9 & 10.5 & **9.7** \\ Jadeplant & 43.0 & 25.1 & 18.8 & **16.1** & 24.2 & 17.4 & 19.7 & 19.0 & 17.4 \\ Motorcycle & 8.9 & 10.0 & 9.0 & **6.8** & 9.6 & **6.8** & 8.5 & **6.8** & 7.6 \\ MotorcycleE & 8.9 & 11.0 & 7.7 & **5.9** & 10.5 & 6.8 & 8.5 & 6.3 & 6.7 \\ Piano & **13.3** & 23.5 & 14.8 & 17.9 & 24.1 & 19.1 & 14.0 & 13.8 & 14.3 \\ PianoL & **25.2** & 32.6 & 26.6 & 26.7 & 32.1 & 34.2 & 29.3 & 25.8 & 27.2 \\ Pipes & 13.8 & 15.5 & 11.6 & 10.1 & 15.1 & 9.4 & 9.9 & 9.4 & **9.1** \\ Playroom & 18.0 & 21.6 & 16.2 & 17.6 & 21.5 & 18.7 & **15.2** & 16.7 & 16.3 \\ Playtable & **10.6** & 39.6 & 18.4 & 27.3 & 39.6 & 24.3 & 16.7 & 20.8 & 16.6 \\ PlaytableP & **6.7** & 20.9 & 10.6 & 12.5 & 20.9 & 11.4 & 11.0 & 10.2 & 10.3 \\ Recycle & **6.8** & 15.0 & 9.1 & 9.1 & 14.4 & 9.5 & 8.3 & 8.1 & 8.7 \\ Shelves & **22.2** & 34.4 & 37.8 & 38.6 & 36.0 & 38.4 & 31.6 & 32.6 & 33.6 \\ Teddy & 9.4 & 7.2 & 6.2 & 6.2 & 7.0 & 6.1 & 5.6 & **5.2** & 6.3 \\ Vintage & 39.3 & 34.4 & 27.3 & 27.5 & 34.6 & 25.6 & 27.0 & **23.9** & 26.5 \\ \hline Weighted Average & 14.7 & 19.3 & 13.6 & 13.9 & 19.1 & 14.0 & 12.9 & 12.5 & **12.4** \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the error rates in the non-occluded region without refinements using different matching algorithms (%). In calculating the error rates, the error threshold \(=1.0\). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Test Sets** & **RTSMNe** & **ISM** & **DDL** & **TPAO** & **IGF** & **ACR-GIF-OW** & **HGIF** & **FASW** & **Proposed** \\ \hline Adiromdack & **6.2** & 18.4 & 8.8 & 11.7 & 17.6 & 12.5 & 8.0 & 8.7 & 7.1 \\ ArtL & **15.5** & 25.6 & 22.8 & 24.5 & 24.8 & 23.4 & 21.2 & 20.8 & 20.7 \\ Jadeplant & 47.8 & 36.8 & 32.4 & **28.4** & 35.9 & 31.3 & 33.0 & 32.3 & 31.8 \\ Motorcycle & **11.7** & 14.9 & 13.4 & 14.1 & 14.5 & 14.9 & 12.6 & 10.9 & 11.9 \\ MotorcycleE & 11.7 & 15.9 & 11.8 & 13.1 & 15.3 & 14.6 & 12.9 & **10.5** & 11.2 \\ Piano & **16.8** & 27.6 & 19.2 & 22.9 & 28.1 & 23.8 & 18.1 & 18.2 & 18.4 \\ PianoL & **28.4** & 36.3 & 30.5 & 30.9 & 35.7 & 37.9 & 33.1 & 29.5 & 30.7 \\ Pipes & 21.7 & 26.2 & 22.7 & 22.8 & 25.7 & 21.8 & 21.5 & **20.2** & 20.7 \\ Playroom & 25.0 & 29.7 & 25.1 & 26.7 & 29.4 & 28.4 & **23.8** & 25.5 & 25.0 \\ Playtable & **13.6** & 42.9 & 23.3 & 33.1 & 42.9 & 30.5 & 21.2 & 25.0 & 22.6 \\ PlaytableP & **8.8** & 25.9 & 14.3 & 19.2 & 25.9 & 18.5 & 14.3 & 13.4 & 14.4 \\ Recycle & **7.8** & 17.5 & 11.4 & 12.7 & 17.0 & 13.6 & 10.3 & 10.2 & 11.2 \\ Shelves & **23.7** & 35.5 & 39.1 & 39.9 & 37.0 & 40.3 & 3 The performance of FASW [21] and the proposed algorithm are more prominent for stereo matching on the dataset. Specifically, in the non-occluded region, the proposed algorithm has the lowest mismatch rate, followed by FASW [21] and the HGIF [20] algorithm. In the all-region, the RTSMNet [25] algorithm has the lowest mismatch rate, followed by FASW [21] and the proposed algorithm. However, we can observe a significant performance deterioration in using RTSMNet [25] to predict the disparity maps of the \"Jadeplant\" and \"Vintage\" image sets. RTSMNet [25] is a deep learning-based method, its generalization ability depends on the quality and richness of the training dataset. Similar scenes may be rare in its training set. This be  
Figure 6: Comparison of the error rates in the non-occluded region. This diagram is a graphical representation of Table 2.  
Figure 7: Comparison of the error rates in the all-region. This diagram is a graphical representation of Table 3.  
havior is not observed in traditional algorithms because their performance is less sensitive to scene type.