# 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  
Feature point extraction and matching in images represent a crucial step in three-dimensional reconstruction. The feature matching phase is executed on the MATLAB platform, utilizing the SIFT algorithm for feature extraction. We employed the KD-Tree matching algorithm based on Euclidean distance to replace the previous brute-force matching approach with a matching threshold set at 0.5. Figure 12 illustrates the SIFT feature detection results. Figure 12 displays two randomly selected original images from the collected data. Figure 12b presents the detection outcomes for these images, which took a cumulative time of 13.467 s. The number of feature points detected, from left to right, were 21,196 and 13,531, respectively. Figure 13 illustrates the differing outcomes of two images after employing two methods of feature matching and error removal. Figure 13a,b present outlier removal results using brute-force (BF) matching and traditional RANSAC, while Figure 13c,d demonstrate the enhanced RANSAC algorithm integrating KD-Tree matching and adaptive threshold adjustment. KD-Tree achieves significantly lower mismatch rates compared to BF; The improved algorithm retains more matched pairs while drastically reducing execution time without compromising accuracy. A comparison of Figures 13a,c reveals that BF exhibits marginally higher per-frame matching accuracy but suffers from substantially more mismatches. These observations validate the rationale for adopting the KD-Tree + SIFT framework, which balances efficiency and robustness while providing precise correspondences for downstream 3D reconstruction tasks. The experimental findings of the performance comparison are shown in Table 4. Experiments reveal significant differences in robustness, matching accuracy, and time efficiency among SIFT-based feature detection with varying matching strategies: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Matched Point** \\ **Pairs** \\ \end{tabular} } & \multicolumn{2}{c}{**Original RANSAC**} & \multicolumn{2}{c}{**Our RANSAC**} \\ \cline{3-6}  & & **Fixed Threshold** & **Optimization of the** & **Adaptive** & **Optimization of the** \\  & & **of the Original** & **Number of Feature** & **Thresholding** & **Number of Feature** \\  & & **Algorithm** & **Match Pairs** & **Match Pairs** & **Match Pairs** \\ \hline Statue1 & 10,316 & 40 & 5050 & 50 & **9136** \\ Statue2 & 3250 & 40 & 1506 & 45 & **2365** \\ Archi1 & 5262 & 40 & 2778 & 65 & **4033** \\ Archi2 & 5703 & 40 & 2859 & 60 & **4893** \\ \hline \hline \end{tabular}  
* The bolded data represents the optimal results for each section.  
\end{table} 
Table 3: Quantitative results of the autonomous threshold adjustment RANSAC algorithm. Figure 12: Feature detection results of the acquired image under Self-Collected Data for Statue1: (a) Original image; (b) Feature detection results. Time Efficiency: The SIFT + Brute Force (BF) baseline takes 84.523 s, increasing to 86.147 s after RANSAC optimization, confirming additional computational costs from iterative sampling. In contrast, SIFT + KD-Tree reduces matching time to 26.634 s (68.5% speedup over BF) through hierarchical spatial partitioning of high-dimensional feature vectors, with only a marginal increase to 26.953 s after enhanced RANSAC, indicating minimal computational overhead during model optimization. Matching Quality: SIFT + BF generates 12,356 initial matches, retaining 5050 pairs after RANSAC filtering, with accuracy improving from 40.871% to 50.50%, demonstrating RANSAC's outlier removal capability. Meanwhile, SIFT + KD-Tree reduces matches from 10,316 to 9136 pairs (11.4% decrease), achieving 88.561% initial matching accuracy after improved RANSAC, significantly outperforming SIFT + BF + RANSAC's 59.1% reduction. This highlights the synergistic effect of SIFT's high discriminative power and KD-Tree's approximate nearest neighbor search, enhancing both precision and stability.