## 3 Experiments
### KITTI Dataset  
KITTI dataset [37; 38] provides real-world testing images with street views taken from a driving car, and most of the images in the dataset contain large portion of homogeneous regions, such as walls and roads. Considering illumination difference and large challenging areas, we adopt two ways to build initial cost volume, namely CT and the correlation of feature maps from PSMNet [40]. Figure 6: Comparison of GF, SGM, MGM and our method on Mibblebury 2014 dataset [36]. Images from top to bottom are the percentage of error pixel and average end-point error in non-occluded region for the 10 testing images. Figure 7: Disparity images of various methods on Middlebury 2014 dataset [36]. Pixels in red and green are mismatched pixels in occluded and non-occluded regions respectively. KITTI 2012 Dataset: Table 4 presents the results of various non-local cost aggregation approaches. Both NL and ST treat reference image as undirected graph and extract the MST by removing edges with large gradients. These two methods tend to overuse piece-wise constant assumption, leading to producing poor results. LDESGM [41] proposes a new local binary encoding pattern based on the intensity relationship between pixels in horizontal, vertical and diagonal directions, and combines this metric with magnitude information to solidify matching cost, then adopts SGM in eight directions to aggregate cost. Our method outperforms LDESGM with a great margin on both metrics, and the average error rates in non-occluded and all regions are reduced by 1.05% and 1.52% respectively. Compare with MGM, the average errors in all and non-occluded regions of our method using CT to compute matching cost are decreased by 1.02 px and 0.45 px respectively. i5 GM [24] iteratively evaluates accumulated cost and intermediate disparity images in scale space to guide the cost aggregation in next pass. Although simpler scheme used in our method, we achieve lower error rate and average disparity error in all region. The gains of i5 GM and w5 GM mainly stem from coarse-to-fine strategy, complicate cost function, multiple refinement steps and subsidiary information. Compare the results of our method using different features to build cost volume, learning-based features generate lower error rate while census transform has smaller average disparity error. This isbecause learning-based features with a large receptive field can reason about local geometry using a wide range of textural information, making these features more robust in a homogeneous area. A handcrafted feature is more accurate than learning-based features to evaluate the similarity of corresponding pixels in high texture regions. We intend to combine the superiority of these two features in building cost volume. Specifically, using a handcrafted feature in a highly textured area preserves fine-scale details and adopting learning-based features in a homogeneous region reduces ambiguity. We will work on this in the future. Figure 8 shows some results of our method on KITTI 2012 dataset [37]. We can see that our approach produces satisfactory disparity images using both features to compute matching cost. Most of the erroneous pixels are located in image borders for disparity images generated by learning-based features, while large errors are in weakly textured areas when using handcrafted feature to compute matching cost. KITTI 2015 Dataset: Table 5 lists the results of state-of-the-art non-local cost aggregation methods on KITTI 2015 dataset. Similar to that in KITTI 2012 dataset, NL and ST generate poor disparity images for overusing of piece-wise constant assumption. SFSGM extends 2D motion information to 3D space by combining stereo matching with optical flow estimation, while the error rates for their method are nearly twice of that for ours. A