# Simplified High-Performance Cost Aggregation for Stereo Matching
## 3 Results
The time required for the matching of a rectified stereo image pair for each algorithm is summarized in Table 4. These computations were executed in MATLAB 2017b using an Intel Core I5 8300 H and 16 GB RAM. Notably, the proposed algorithm takes the least computational time. If we consider both matching accuracy and algorithmic complexity, the proposed algorithm is close to the best accuracy while requiring the least amount of computation. To verify the stereo matching performance of the proposed algorithm in real scenes, the autonomous driving training dataset of KITTI Vision Benchmark Suite [32] is used for further demonstration. The dataset contains 194 image pairs with corresponding ground truth disparity maps. Performance comparisons to be presented are between the following stereo matching schemes:  
1. Stereo matching based on adaptive guided filtering [13], denoted as AGF. 2. Adaptive stereo matching using tree filtering [17], denoted as MST. 3. The ISM algorithm [15]. 4. The HGIF algorithm [20]. 5. The FASW algorithm [21]. 6. The proposed scheme. Figure 8 shows the disparity maps generated by these algorithms on the KITTI suite [32]. Each algorithm performs stereo matching on 194 sets of stereo image pairs. Due to space constraints, we only select three stereo-image pairs for visual comparison. Table 5 shows the quantitative matching performance of these algorithms. Four scores are compared: the percentage of erroneous pixels in non-occluded areas, denoted as Non-Occ (%); the percentage of erroneous pixels in all regions, denoted as All (%); the average disparity error in terms of pixel numbers in non-occluded areas, denoted as Non-Occ (pixels); and the average disparity error in terms of pixel numbers in all regions, denoted as All (pixels). The scores used for comparison are quoted from [21]. \begin{table}
\begin{tabular}{c c c c} \hline
**Test Sets** & **ACR-GIF-OW** & **HGIF** & **Proposed** \\ \hline Adirondack & 215 & 31 & **10** \\ ArtL & 34 & 7 & **1** \\ Jadeplant & 374 & 87 & **21** \\ Motorcycle & 191 & 37 & **10** \\ MotorcycleE & 192 & 36 & **10** \\ Piano & 169 & 27 & **7** \\ PianoL & 171 & 26 & **7** \\ Pipes & 197 & 37 & **11** \\ Playroom & 211 & 31 & **11** \\ Playtable & 169 & 29 & **9** \\ PlaytableP & 167 & 30 & **9** \\ Recycle & 199 & 30 & **8** \\ Shelves & 195 & 29 & **8** \\ Teddy & 68 & 14 & **4** \\ Vintage & 499 & 98 & **25** \\ \hline \end{tabular} The best score for each image set is marked in bold.  
\end{table} 
Table 4: Comparison of the running time of each algorithm for a pair of rectified images (s). These computations were executed in MATLAB 2017b using an Intel Core I5 8300H and 16 GB RAM. As seen from Figure 8 and Table 5, compared with the five representative aggregation methods, the proposed algorithm outperforms other algorithms in items Non-Occ (%), All (%), and Non-Occ (pixels), with scores of 6.30%, 7.48%, and 1.3 pixels, respectively. Its performance is only slightly inferior to [21] on All (pixels), with a score of 1.58, which is higher than [21]'s 1.45. Figure 9 shows three disparity maps generated by the proposed algorithm using three sets of multi-view remote sensing images (pictures from [[https://github.com/whuwuteng/benchmark_ISPRS2021](https://github.com/whuwuteng/benchmark_ISPRS2021)]([https://github.com/whuwuteng/benchmark_ISPRS2021](https://github.com/whuwuteng/benchmark_ISPRS2021)), accessed on 15 February 2019). These additional disparity maps demonstrate the effectiveness of the proposed approach. Figure 8: Visual comparison of disparity maps obtained by different algorithms using the KITTI Vision Benchmark Suite [32]. (**a**) Three of the left scene images selected from the dataset. (**b**) Ground-truth disparity maps. (**c**) Disparity maps generated by ISM [15]. (**d**) Disparity maps generated by AGF [13]. (**e**) Disparity maps generated by MST [17]. (**f**) Disparity maps generated by HGIF [20]. (**g**) Disparity maps generated by FASW [21]. (**h**) Disparity maps generated by the proposed scheme.