# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 4 Results
### Experimental Results and Analysis
#### 4.2.4 Improving the Analysis of Sparse Reconstruction Results in Algorithms  
This section seeks to fully illustrate the influence of the suggested similarity measure enhancement on the incremental SFM sparse reconstruction algorithm, building on the analysis of the view improvement algorithm's efficacy. We will compare the enhanced algorithm's performance with other popular algorithms like VSFM [24], COLMAP [7], and SfSM [25] and look into how well it performs in sparse reconstruction. We will evaluate the algorithms based on the number of point clouds and the accuracy of the reconstruction results by comparing and contrasting the sparse reconstruction results before and after the improvements. Figures 17-20 illustrate the comparative sparse reconstruction results of the proposed algorithm against various other algorithms using the self-collected data from Statue1, Statue2, Archi1, and Archi2. Figure 19: Sparse reconstruction results of the self-collected data Archil under various algorithms. Figure 18: Sparse reconstruction results of the self-collected data Statue2 under various algorithms. The presentation format of the results is as follows: from left to right, the front, side, and rear views of the reconstruction results are displayed. This three-view representation provides a clear visual demonstration of the accuracy and completeness of the sparse reconstruction from a subjective visual perspective. All results are saved in '.ply' format and analyzed using the Cloud Compare software v2.13.2. As illustrated in Figure 17, the sparse reconstruction results for the self-acquired dataset Statue1 include SfSM, VSFM, COLMAP, and the improved algorithm proposed in this paper. In terms of point cloud density, the algorithm presented herein produces a more densely populated point cloud that covers a significant portion of the statue, whereas the other three methods yield relatively sparse point clouds with numerous voids and missing areas. This indicates that the proposed algorithm is more effective in feature point extraction and retains a greater amount of valid information during the matching process, resulting in a richer point cloud dataset. The algorithm presented in this paper generates statue models with greater accuracy in geometric shapes and details, successfully recovering more surface textures and contours. In contrast, the sparse reconstruction results from the SfSM and VSFM algorithms exhibit noticeable distortion or deformation, while the COLMAP algorithm performs relatively well; however, the proposed algorithm demonstrates superior results. This enhancement is attributed to the optimization of the incremental structure from the Motion (SfM) sparse reconstruction algorithm and improvements in the initial feature matching phase. Comparative analysis of the supplementary data in Figures 18-20, specifically for Statue2, Archi1, and Archi2, substantiates the significant advantages of the proposed algorithm in terms of reconstruction accuracy and point cloud density. Through a comprehensive analysis of sparse reconstruction results from four self-collected datasets (Statue1, Statue2, Archi1, Archi2), the following conclusions are drawn: In terms of point cloud quantity and reconstruction accuracy, the proposed enhanced algorithm demonstrates significant advantages over traditional methods (e.g., SfSM, VSFM,  
Figure 20: Sparse reconstruction results of the self-collected data Archi2 under various algorithms. COLMAP). The improved algorithm substantially increases the number of generated point clouds, achieving a more uniform distribution that nearly covers all areas of the scene, indicating its superior capability in extracting and utilizing image feature information to produce denser point cloud data. Furthermore, the enhanced algorithm excels in detail reconstruction, more accurately capturing subtle scene structures and textures, thereby improving the precision of geometric shapes and intricate details with a clear representation of scene contours and structures.