# Simplified High-Performance Cost Aggregation for Stereo Matching
## 3 Results  
To investigate the matching performance of the proposed scheme, performance comparisons have been made between eight representative stereo matching schemes and the proposed scheme:  
1. End-to-end real-time stereo matching network proposed in [25], denoted as RTSMNet. 2. Matching algorithm based on a combination of the adaptive support weight with iterative guided filter and the sum of gradient matching [15], denoted as ISM. 3. Sparse representation over a learned discriminative dictionary for stereo matching [4], denoted as DDL. 4. Stereo matching algorithm based on two-phase adaptive optimization of ad-census and gradient fusion [8], denoted as TPAO. 5. Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter, and graph segmentation [14], denoted as IGF. 6. Local stereo matching using adaptive cross-region-based guided image filtering with orthogonal weights [16], denoted as ACR-GIF-OW. 7. Hierarchical guided-image-filtering for stereo matching [20], denoted as HGIF. 8. Stereo matching with fusing adaptive support weights [21], denoted as FASW. 9. The proposed scheme, which is an integration with a cross-scale cost aggregation algorithm as described in the last section. We selected several stereo image pairs from the Middlebury version 3 [31] and the KITTI Vision Benchmark Suite [32] datasets for demonstration. The \"trainingQ\" of the Middlebury version 3 [31] dataset is composed of 15 groups of pictures from \"Adiromadex\" to \"Vintage\". As summarized in Table 1, resolutions of the images are around 480-by-720. According to the previous section's discussion, the design parameter is positively related to the image resolution under matching. In the following demonstrations, the values of \(\lambda\) are simply assigned as  
\[\lambda=\frac{M}{480}\cdot\frac{N}{720}\cdot 6 \tag{26}\]  
Figure 5 shows the disparity maps obtained by these stereo matching algorithms on four of the image sets. Tables 2 and 3 present the error rates and weighted error rates of the algorithms using the complete \"trainingQ\" of the Middlebury version 3 [31] dataset. In the tables, the experimental results of the four algorithms for comparison are obtained from the original literature. For ease of viewing, the graphical representations of Tables 2 and 3 are shown in Figures 6 and 7. \begin{table}
\begin{tabular}{c c c c c c c c} \hline Resolution & Adirondack & ArtL & Jadeplant & Motorcycle & MotorcycleE & Piano & PianoL & Pipes \\ \hline M & 496 & 277 & 497 & 497 & 497 & 481 & 481 & 485 \\ N & 718 & 347 & 659 & 741 & 741 & 707 & 707 & 735 \\ \hline Resolution & Playroom & Playtable & PlaytableP & Recycle & Shelves & Teddy & Vintage \\ \hline M & 476 & 463 & 462 & 486 & 497 & 450 & 480 \\ N & 699 & 680 & 681 & 720 & 738 & 375 & 722 \\ \hline \end{tabular}
\end{table} 
Table 1: Resolution of the images for stereo matching. Figure 5: Visual comparison of disparity maps obtained by different algorithms. (**a**) The left scene images selected from the Middlebury version 3 [31] dataset (from left to right): Adirondack, Jadeplant, Playroom, and Playtable. (**b**) Ground-truth disparity maps, (**c**) Disparity maps generated by RTSMNET [25]. (**d**) Disparity maps generated by ISM [15]. (**e**) Disparity maps generated by DDL [4]. (**f**) Disparity maps generated by TPAO [8]. (**g**) Disparity maps generated by IGF [14]. (**h**) Disparity maps generated by ACR-GIF-OW [16]. (**i**) Disparity maps generated by HGIF [20]. (**j**) Disparity maps generated by FASW [21]. (**k**)Disparity maps generated by an integration of the proposed scheme.