## 3 Experimental Results and Analysis
### Experimental Results for Feature Extraction with Depth Value Calculation
We chose three types of algorithms--SURF and SIFT, which had higher matching success rates, and ORB, which had slightly lower time consumption--to perform feature detection and feature description, respectively, and then used the FLANN feature matching algorithm to perform comparison experiments based on Euclidean distance. The disparity map derived after the stereo-matching experiment was mechanically extracted from the feature points, and then the 3D coordinates were calculated to finally reconstruct the 3D point cloud for the same scenes, as shown in Figure 15.  
It can be seen that, for unstructured and large scenes with significant differences in the intensity of texture information in different regions, the traditional algorithms selected too few feature points and the feature information contained was not optimal, resulting in poor point cloud effect maps that could no longer be triangulated to achieve a complete and effective structured network and, therefore, could not meet the requirements of high-precision 3D models. We conducted 3D reconstruction real-time performance tests using the three scene images taken with the stereo-camera platform, and the average test data for the images are shown in Table 3. It can be seen that our algorithm showed a large improvement in real-time performance over the traditional, widely used 3D reconstruction techniques when reconstructing such unstructured scenes with intricate texture information, and it can meet the high real-time performance standard. Figure 15: Point cloud maps produced by the conventional algorithms for three scenes: (**a**) SURF [58]; (**b**) SIFT [57]; (**c**) ORB [61]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**3D Reconstruction Steps**} & \multicolumn{2}{c}{**Traditional Algorithms/ms**} & \multicolumn{2}{c}{**Our Algorithm/ms**} \\ \cline{2-5}  & SURF [58] & SIFT [57] & ORB [61] & SAD-FAST + SURF + GVDS \\ \hline Camera and image pretreatments & 47.361 + 51.12 & 47.361 + 51.12 & 47.361 + 51.12 & 47.361 + 51.12 \\ Stereo-matching experiment & 336.27 & 182.61 & 61.632 & 64.301 \\ Depth value calculation & 57.94 & 42.46 & 25.73 & 17.82 \\ Triangulation and texture mapping & — & — & — & 35.134 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Real-time testing of different traditional algorithms. in forming matches between image pairs and, eventually, the formation of disparity maps of very poor quality. The 3D point clouds formed by feature extraction on the basis of these disparity maps were so sparse that effective mesh skeletons could not be constructed, which was the reason why it was difficult to form the final models of texture mapping after feature extraction. The disparity maps generated by the algorithm from [62] had high numbers of voids in the junction areas of both complex and weakly textured regions of texture information in all three scenes. Although the areas with the voids were small compared to the whole disparity maps, the overconcentrated distribution led to the loss of 3D information when feature extraction was performed, and eventually voids or black impurities that did not have texture were formed in the texture models, as shown in the red boxes in Figure 16b. The disparity maps generated by the algorithm from [63] also had a few voids in the ground of scene one, and this drawback was also reflected in the final 3D model. The disparity maps generated by the algorithm from [64] did not show voids in weakly textured regions in any of the three scenes, nor did they show large voids in adjacent regions due to overly dense distribution of strong information feature points in texturally complex regions. In our proposed stereo-matching algorithm, the upper left corner of the ground in scene one had a very small number of voids compared to the algorithm from [64], but the area and size of the voids were smaller than those with the other three algorithms.