# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 4 Results
### Experimental Results and Analysis
#### 4.2.2 Feature Detection and Matching
Overall, the SIFT + KD-Tree + enhanced RANSAC approach provides a solution that supports both real-time performance and robustness for large-scale image registration tasks by striking an ideal balance between time efficiency (26.953 s), matching accuracy (88.561%), and false match suppression capability (false match rate \(<\) 11.5%). Figure 14 illustrates the comparative results of various feature-matching algorithms applied to self-collected data. Figure 14 shows the matching outcomes of four feature matching algorithms on four sets of self-collected data: SURF [22], SIFT [19], MS-HLMO [23], and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithmic Ensemble** & **Time/s** & \
\begin{tabular}{c} **Number of** \\ **Matched Point** \\ **Pairs** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy of** \\ **Matches\(\uparrow\)** \\ \end{tabular} \\ \hline SIFT + BF & 84.523 s & 12,356 & \\ SIFT + BF + RANSAC & 86.147 s & 5050 & \\ SIFT + KD-Tree & 26.634 s & 10,316 & \\ SIFT + KD-Tree + our RANSAC & **26.953 s** & **9136** & **88.561\%** \\ \hline \hline \end{tabular}  
* The arrow indicates that a higher match accuracy is preferable; the bolded data represents the optimal results for each section.  
\end{table} 
Table 4: Performance of SIFT Algorithm Matching under Self-Collected Data Statue1. Figure 13: Comparison of SIFT Algorithm Matching Performance under Self-Collected Data for Statue1: (**a**) the results of SIFT + BF; (**b**) SIFT + BF + RANSAC; (**c**) SIFT + KD-Tree; (**d**) SIFT + KD-Tree+ our RANSAC, The green line represents the optimization results of the original RANSAC algorithm, while the red line indicates the increase in the number of matched point pairs after optimization using the proposed algorithm in this paper. our suggested approach. It is evident that our algorithm is capable of preserving a greater number of matching point pairs. Tables 5 and 6 present a comparative analysis of the proposed algorithm against various matching algorithms, focusing on the number of matched point pairs and accuracy metrics across four sets of self-collected data. Based on a thorough examination of Tables 5 and 6, we can determine that our algorithm performs noticeably better than competing algorithms in two crucial metrics: the accuracy of matching and the quantity of matched point pairs. In particular, throughout all datasets, our algorithm produces a significantly greater number of matched point pairs; however, it excels in the Archi1 dataset, successfully matching 4090 point pairs. With a maximum accuracy of 88.56% (Statue1) and a minimum of 64.27% (Archi2), our algorithm also performs exceptionally well in terms of matching accuracy across all datasets, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Matching** & \multicolumn{4}{c}{**Matching Accuracy\(\uparrow\)**} \\ \cline{2-5}
**Algorithm** & **Statue1** & **Statue2** & **Archi1** & **Archi2** \\ \hline SIFT & 40.87\% & 52.46\% & 33.54\% & 50.28\% \\ SURF & 1.97\% & 6.54\% & 1.26\% & 1.86\% \\ MS-HLMO & 55.14\% & 23.91\% & 22.34\% & 27.21\% \\ Ours & **88.56\%** & **75.35\%** & **71.72\%** & **64.27\%** \\ \hline \hline \end{tabular}  
* The higher the data values, the better; the bolded data represents the optimal results for each group.  
\end{table} 
Table 6: The accuracy of various matching algorithms in four sets of self-collected data. Figure 14: Matching results of various feature matching algorithms in self-collected data, The green line shows original algorithm results; the yellow line, comparative algorithm matches; the red section highlights additions from this paper’s optimized algorithm.