# Hierarchical Guided-Image-Filtering for Efficient Stereo Matching
## 3 Experimental Results
Among them, only the results of SRDD [13] were improved by the disparity refinement procedure. The corresponding error rates in the non-occluded region and the all-region are summarized in Tables 1 and 2, respectively. Taking a close look of the disparity maps of Figure 4, we have that there was a significant improvement in the matching quality of CS-FCVF [19; 29] over FCVF [19], especially in the texture-less regions of the \"Playtable\" and \"Shelves\" cases. This improvement was less significant but could also be observed in that of the CS-PGIF [25; 29] over PGIF [25]. These improvements were due to the effective cross-scale cost aggregation scheme of Reference [29]. According to the weighted average error rates listed in Tables 1 and 2, DSG [8] performed worst. Also, the matching performance of SRDD [13] was better than CS-FCVF [19; 29] but slightly worse than CS-PGIF [25; 29]. It is worthy to note that SRDD [13] applies semi-global cost aggregation and post-processing refinement to further improve the matching accuracy. However, the proposed scheme, even without refinement, performed better than SRDD [13] in most of the cases and achieves the smallest weighted average error rates in both the all-region and the non-occluded (non-occ) region. The experiment was executed in MATLAB 2017b using an Intel Core I5 8300H and 16 GB RAM. Table 3 summarizes the execution time of these algorithms. We have that the multi-scale versions, CS-FCVF [19; 29] and CS-PGIF [25; 29], required more time for computation than their original versions, FCVF [19] and PGIF [25], respectively, as expected. Figure 3: Datasets and their corresponding ground truth disparity maps selected from the experimented data for visual comparison. (**a**) Left images of the image pairs: Adiromdack, Pipes, Playroom, Playtable, and Shelves; (**b**) ground truth disparity maps of these images. Image courtesy of the Middlebury (version 3) benchmark stereo database via the URL: vision.middlebury.edu/sterero/ [31]. Similarly, the proposed algorithm needed to calculate the filtering parameters in multiple scale layers, it ran longer than the FCVF algorithm [19] and the PGIF algorithm [25]. In addition, the CS-FCVF [19; 29] and CS-PGIF [25; 29] algorithms only incorporated one matching cost parameter, while the proposed algorithm has to compute two parameters, \(\hat{a}_{d,0}(p)\) and \(\hat{b}_{d,0}(p)\) in Equations (11) and (12). We also have that the proposed algorithm ran slightly longer than CS-FCVF [19; 29] and CS-PGIF [25; 29]. However, the increased computation time was marginal and still within the same order of magnitude. Moreover, both DSG [8] and SRDD [13] required much more computational resource than the other algorithms due to their deep neural network and semi-global cost aggregation scheme, respectively, as pointed out in Reference [13]. Based on Table 3, we may conclude that the proposed scheme had the best performance when taking both the matching correctness and the calculation efficiency into consideration. Figure 4: Comparisons of disparity maps obtained by different algorithms. All the results are obtained without the refinement procedure except SRDD (The sparse representation over discriminative dictionary scheme) [13]. (**–g**) are disparity maps generated by: (**a**) FCVF (The fast cost volume filtering scheme) [19], (**b**) CS-FCVF (A combination of the cross-scale cost aggregation scheme [29] and FCVF), (**c**) PGIF (The pervasive guided image filter scheme) [25], (**d**) CS-PGIF (A combination of [29] and PGIF), (**e**) DSG (The deep self-guided cost aggregation scheme) [8], (**f**) SRDD (The sparse representation over discriminative dictionary scheme) [13], and (**g**) the proposed scheme. Among them, the images of (**e**) DSG and (**f**) SRDD are by courtesy of the Middlebury (version 3) benchmark stereo database via the URL: vision.middlebury.edu/stereo/ [31].