# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 3 Materials and Methods
### Enhanced Incremental SFM Sparse Reconstruction Algorithm for Similarity Measurement
#### 3.3.2 Image Continuity Optimization Adjustments  
After autonomous strategy selection, the matching relationship between images is determined, and the process of SIFT feature point detection, KD-Tree feature point matching, and RANSAC mismatch filtering are carried out to determine the two-dimensional correspondence between images with overlapping degrees. After the initial filtering of the primary view through similarity metrics, the data collected across different time periods exhibits intra-class similarity (such as repetitive textures and symmetrical structures). Consequently, even after preliminary selection, some spurious matches may persist, which  
Figure 6: Schematic diagram of the improved incremental SFM algorithm for similarity measurement.  
Figure 7: Flow chart of feature matching autonomous selection strategy.  
can still impact the accuracy of the reconstruction. The specific algorithm flowchart for optimizing image continuity is illustrated in Figure 8.  
After obtaining the initial matching views based on similarity metrics, this study introduces a temporal continuity constraint mechanism. By analyzing the temporal correlations within the image acquisition sequence, the preliminary matching results are optimized and refined, filtering key matching views that adhere to physical scene constraints, thereby establishing robust matching relationships.  
Addressing the issue of reduced accuracy in reconstruction models caused by mismatches arising from temporally disparate yet structurally similar data (where the time intervals of image acquisition are significant but the similarity metrics are high). As illustrated in Figure 9.  
Figure 9 illustrates the two criteria that control the image's continuity:  
The temporal continuity algorithm optimizes matching relationships through a dual mechanism: (1) it mitigates the interference of similar structures across time periods on reconstruction accuracy (see Figure 9a); (2) it ensures that essential matches between the first and last images in a circular acquisition scheme are preserved and not discarded by the temporal continuity algorithm (see Figure 9b). To prevent the loss of reliable matching  
Figure 8: Flowchart for Image Continuity Optimization Adjustment Algorithm.  
Figure 9: Optimization and adjustment algorithms for enhancing image continuity issues: (**a**) The structural similarity of cross-temporal images; (**b**) The images exhibit temporal coherence in the context of the immersive acquisition framework.  
relationships due to algorithmic improvements, this study employs a segmented temporal filtering strategy: on the one hand, it conducts continuity assessments based on a temporal matrix to segment images by viewpoint, thereby eliminating cross-period interference; on the other hand, it establishes a threshold for the first and last images, mandating the retention of critical matches in the circular shooting sequence to maintain the topological integrity of the scene.  
In the incremental SFM sparse reconstruction process, the two images with the highest number of matching feature points are first selected, and the external parameters of one image are set as the identity matrix. Then, the position matrices of the two cameras are solved using the five-point method, and the obtained feature points are traced in the images. The two-dimensional feature points are triangulated to obtain their corresponding three-dimensional coordinates, and the obtained three-dimensional coordinates are optimized using Bundle adjustments (BA). To complete this process, the next image is added, and the above steps are repeated until all images are added. By improving the incremental SFM sparse reconstruction method, more accurate two-dimensional corresponding feature points can be obtained, and the problem of ambiguous matching caused by structural similarity between images can be avoided, thus improving reconstruction model accuracy.