# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 4 Results
### Experimental Results and Analysis
#### 4.2.3 Analysis of the Effectiveness of View Improvement Algorithms  
To address the issue of numerous invalid matching relationships in the incremental SFM sparse reconstruction algorithm, the influence of similar structures across different time periods can diminish reconstruction accuracy. This paper proposes a feature-matching strategy based on similarity measures, which extracts the similarity of images and conducts an initial screening of matching relationships. By considering the temporal continuity of image acquisition, the initial matching views are optimized and adjusted to generate  
Figure 15: Quantitative analysis of various matching algorithms in self-collected data: (**a**) The number of matched point pairs across four sets of self-collected data using various matching algorithms; (**b**) The accuracy of various matching algorithms in four sets of self-collected data.  
more complete and accurate matching views. The objective is to enhance the quantity and accuracy of point clouds in model reconstruction. A comparison with the COMAP algorithm, the matching view comparison results of COLMAP and the improved algorithm presented in this paper are illustrated in Figure 16.  
Figure 16 presents matching views in the Structure from Motion (SfM) algorithm using network graphs, where nodes represent viewpoints and edges indicate matching relationships. Node size and edge thickness may scale with the number of matching pairs or quality, visually reflecting differences in view construction across algorithms. The first row (Figure 16(a1,b1,c1,d1)) shows matching views from the traditional COLMAP algorithm, revealing a high density of edges and well-organized views, indicating equal treatment of all images. However, similar structures across different time periods can cause ambiguous matches, reducing reconstruction accuracy, and feature matching on images without common areas leads to redundancy. In contrast, the second row (Figure 16(a2,b2,c2,d2)) displays matching views from the improved incremental SFM sparse reconstruction algorithm proposed in this study, with significantly fewer edges and more targeted view distribution. This demonstrates that the improved algorithm establishes more matches between highly similar images, forming denser combinations. The enhanced algorithm not only reduces redundant matches but also more accurately filters valid matching relationships, providing a reliable foundation for improving reconstruction quality and precision.  
The specific performance improvements of the algorithm presented in this paper, as demonstrated in the matching view, are illustrated in Table 7.  
The number of edges in the matching view, represented by \(N_{C}\) in this case, is equal to the number of iterations needed for further feature-matching tasks. According to the table's data, the enhanced algorithm's matching iterations for every dataset are noticeably fewer than those generated by the original algorithm. In particular, there are 63.4%, 76.3%, 76%, and 84.2% decreases in matching iterations between Stat  
Figure 16: Comparison of matching views before and after improvements to the COLMAP algorithm. (**a1**,**b1**,**c1**,**d1**) represent the matched views of the original COLMAP algorithm across four datasets.; (**a2**,**b2**,**c2**,**d2**) is a representation of the matching views of the enhanced method described in this paper across four datasets.  
Strong generalizability and applicability are demonstrated by the improved algorithm's impressive performance across a variety of dataset types, including sculptures and architecture. The enhanced algorithm successfully lowers the number of matches, demonstrating its stability and effectiveness in managing datasets of different sizes, whether used on small datasets like Statue1 with only 30 images or large datasets like Archi2 with 100 images.