# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 5 Conclusions  
Addressing the issues of limited point cloud density, model inaccuracies, and poor completeness inherent in existing multi-view image 3D reconstruction algorithms, this paper proposes an enhanced algorithm. Under conditions of comparable temporal efficiency to most current algorithms, experimental validation using four sets of self-collected data demonstrates that the proposed reconstruction algorithm increases the point cloud density by nearly 2.3 times, improves model accuracy by approximately 20%, and enhances completeness by 45%. The resulting model exhibits a greater quantity of point clouds that are more accurate and complete. The primary methods and contributions of this study are outlined as follows:  
1. To address the issues of low correct point pair matching and accuracy associated with traditional feature matching algorithms, this study proposes an enhanced RANSAC optimization scheme that integrates SIFT feature extraction, KD-Tree accelerated matching and autonomous threshold adjustment. SIFT is particularly effective in extracting a substantial number of stable feature points in complex textured scenes due to its strong robustness against scale, rotation, and illumination variations. Meanwhile, KD-Tree improves matching speed and reduces computational complexity through efficient spatial partitioning, contrasting with the O(N\({}^{2}\)) complexity of brute-force methods. By incorporating dynamically adjusted thresholds in RANSAC to optimize the exclusion of mismatches, we observed significant improvements in both the number of matched point pairs and accuracy across four datasets. For instance, in the Statue1 dataset, the number of effective matches surged from 5050 pairs using SIFT to 9136 pairs, with accuracy increasing from 40.87% to 88.56%, significantly outperforming algorithms such as SURF and SIFT, thereby providing high-quality sparse point cloud inputs for generating more detailed 3D models;
2. This study indicates a two-stage optimization method to address the model distortion problems resulting from similar structures across different time frames and the computational inefficiencies brought on by exhaustive matching in traditional incremental Structure from Motion (SfM): Prioritizing the matching of images from adjacent time frames (interval < 5 frames) will eliminate contradictory matches from similar structures across different time periods. Matching selection through similarity metric: using global feature extraction and a dynamic threshold (similarity > 0.75), we achieve a 70% reduction in redundant computations, preventing erroneous matches due to repetitive textures. According to experimental results, the suggested approach greatly increases the number of sparse reconstruction point clouds by almost 2.3 times.  
Conceptualization, L.Y. and H.G.; methodology, H.G. and Z.Y; software, W.W.; validation, J.H. and L.G.; formal analysis, L.W.; investigation, L.Y.; data curation, Y.L.; writing--original draft preparation, H.G.; writing--review and editing, Z.C.; visualization, H.G.; supervision, L.Y.; project administration, L.G.; funding acquisition, L.Y. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The Science and Technology fund project of Shaanxi Provincial Department [2023-YBGY-369], Key Scientific Research Plan of Education Department of Shaanxi [23]Y035], The Youth Innovation Team of Shaanxi Universities [K20220184].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, [[EMAIL_ADDRESS]], upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.