## 3 Experimental Results and Analysis
### Experimental Results for Feature Extraction with Depth Value Calculation  
Figure 10 shows three image pairs acquired with our stereo camera experiment platform. The acquisition areas were concrete pavements in different natural scenes. We performed stereo matching based on the proposed SAD-FAST feature detection, SURF feature descriptor, and FLANN matching algorithms, combined with an overall strategy of discarding feature points that were too close together, followed by extracting features and calculating depth values. The disparity results are shown in Figure 11.  
Figure 11: Disparity results for the three scenes. Figure 10: Left and right views of the scenes. (**a**) Left view: (**b**) right view. Figure 9: Comparison of experimental results for matching with several feature extraction algorithms using the FLANN matcher: (**a**) ORB [61]; (**b**) SIFT [57]; (**c**) SURF [58]; (**d**) AKAZE [54]; (**e**) BRISK [55]; (**f**) ORB + SURF combination: (**g**) AKAZE + SURF combination: (**h**) SIFT + SURF combination: (**i**) BRISK + SURF combination: (**j**) KAZE + SURF combination: (**k**) SAD-FAST + SURF combination. It can be seen that our disparity result maps connected very smoothly in the at areas with weak textures, showing good results without any large voids. Figure 12a shows the results of the feature extraction for the three scenes when it was dif cult to extract feature points from at areas. The distributions of the feature points in the gures were mostly concentrated in areas with large gradient changes; that is, bushes, crops, trees, and areas where the road surface and strong texture information divided in the original gures. For the road surface, very few feature points were extracted in the slightly wider area of the existing crack defect site. For most of the at areas with weak depth variation and narrow fracture areas, it was not possible to extract feature points, resulting in serious loss of 3D information in such areas. Figure 12b shows the results of feature point extraction with our proposed GVDS feature extraction algorithm. It can be seen that not only did it satisfy the requirement for more feature points in the region with strong texture information but it also extracted feature points in the region with weak depth variation, thus avoiding the loss of 3D information in this region and achieving the overall desired 3D reconstruction effect. Figure 13a shows the effect for the 3D point cloud scene when it was not possible to extract the feature points of the at area, and Figure 13b shows the effect for the 3D point cloud scene after the feature points were extracted using our algorithm. Figure 14a shows the nal reconstruction effect for the unextracted at area after texture mapping, Figure 14b shows the reconstruction effect for the extracted at area after texture mapping, and Figure 14c shows the top view of the reconstructed at area. It can be seen that, in Figure 14a, large areas of 3D information were lost due to the inability to extract feature points, so the reconstruction effects for such critical parts as cracks and pavements were extremely poor, which is what directly leads to the dif cult of forming a visualized 3D model with integrity and practicality. As can be seen in Figure 14b, our proposed algorithm retained the feature points with important texture information while extracting the feature points in the nearly texture-free area so that the 3D information for such areas could be obtained and the nal 3D model built. At the same time, we combined the reconstruction with the top view in Figure 14c, and the reconstruc  
Figure 12: Schematic diagram of feature point extraction results: (a) non-GVDS; (b) GVDS. The reader can refer to Table 2 for a comprehensive comparison of traditional algorithms.