## 3 Experimental Results and Analysis
### Experimental Results for Feature Extraction with Depth Value Calculation
Due to the low number and small area, feature points could still be extracted around the tiny voids without losing this part of the 3D information when reconstructing using our proposed GVDS algorithm. For scene two, our proposed algorithm did not show voids in the road surface, while for the junction parts of the road surface and the field canyon, due to the fineness of the weeds themselves and obscuration, the algorithm from [63], the algorithm from [64], and our algorithm all showed voids in different locations in the same area in the green box in scene two. However, since this region was not a large area with continuous disparity loss, 3D information could still be obtained with the 3D reconstruction using GVDS. In scene three, neither the algorithms from [63; 64] nor our proposed algorithm showed voids, but the algorithm from [62] showed a too obvious hole in the junction parts. In summary, the disparity effect developed by the algorithm from [62] was significantly weaker than that of the other three algorithms when they were utilized for disparity comparison. The algorithm from [63] was weaker than the algorithm from [64] and our proposed algorithm in some regions. Our proposed algorithm showed very few voids in the upper left region of the ground in scene one and, similarly to the disparity maps formed by the algorithm from [64], it showed good disparity results in other regions of scene one and in the rest of the scenes. At the same time, it should also be noted that many of the stereo-matching algorithms proposed so far have shown excellent results compared to traditional algorithms when dealing with individual cases with almost limitless strong texture information (e.g., countless leaves along with a single leaf with a very small area compared to the overall area of the image), but it is also difficult for them to avoid a few voids, which can be studied further in the future. We performed feature extraction using GVDS for the disparity maps formed by the three algorithms from [62; 63; 64] on the basis of Figure 16 and finally formed 3D models, as shown in Figure 17.  
It can be seen that, in scene one in Figure 17a, because the disparity map of the algorithm from [62] had more concentrated and slightly more numerous holes in the junction area between the road surface and the bushes, distortion of the fine line pattern of the road surface and of the schoolbag ultimately resulted during texture mapping. In scene one in Figure 17a, firstly, the disparity map formed by the algorithm from [62] had a large number of large voids on the road surface, which directly led to the presence of a large number of black impurities on the road surface in the 3D model. It can also be seen that there were some yellow voids in the area with crops in the field canyons, which were caused by the absence of point clouds. In scene three in Figure 17a, the 3D model formed by the algorithm from [62] had a slight hollow in the depths of the leaves. For the 3D model formed with the algorithm from [63], there were more black impurities on the pavement in scene one due to the existence of a disparity map with a wide distribution of fine voids on the pavement. For scene two, the 3D model had very few cavities in the crop area of the field canyon. In scene three, there was a cavity in the ground and foliage junction area. In the 3D models of the three scenes formed by the algorithm from [64], only a few black impurities appeared in some areas of the road surface in scene one, and the remaining two scenes were well-reconstructed.