# Dense Robust 3D Reconstruction and Measurement for 3D Printing Process Based on Vision
## 5 Comparison and Analysis of 3D Reconstruction Experiments in 3D Printing Process
### Experimental and Comparative Analysis of FFT-SIFT Algorithm for Feature Point Extraction
Experimental Comparison and Analysis of Feature Point Extraction and Matching Based on FFT-SIFT-AKAZE Algorithm  
The previous experiments show that the FFT-SIFT algorithm has the advantages of scale rotation invariance, but the extraction effect of image edge contour features is limited, and the detailed acquisition of edge information is of great significance to the accurate reconstruction of 3D printed parts. Therefore, the AKAZE algorithm and the FFT-SIFT algorithm are fused to extract and match the feature points for comparative experiments to verify the effectiveness of the fusion method. The part of the images obtained from image acquisition in the printing process are used for experimental comparative analysis. Figure 8a is the result of feature extraction by the FFT-SIFT algorithm, Figure 8b is the result of feature extraction by the AKAZE algorithm, and Figure 8c is the result of feature extraction by the FFT-SIFT-AKAZE algorithm. The comparison shows that AKAZE is more sensitive to edge contour information, and more detailed feature point information can be extracted by combining the two algorithms. Figure 9 is the verification of the feature matching results of the three algorithms. Figure 9a-c are the effect pictures of the FFT-SIFT algorithm, AKAZE algorithm and the FFT-SIFT-AKAZE algorithm for feature matching, respectively. \begin{table}
\begin{tabular}{c c c} \hline
**The Image Set Corresponds to the Print** & **Time Consumption of SIFT Algorithm** & **Time Consumption of Feature Points** \\
**Progress** & **to Extract Feature Points** & **Extraction by FFT-SIFT Algorithm** \\ \hline
23\% & 98.18 s & 74.91 s \\
36\% & 99.54 s & 75.61 s \\
63\% & 97.23 s & 73.37 s \\
85\% & 102.35 s & 74.17 s \\
100\% & 93.36 s & 70.25 s \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of feature extraction time of different algorithms in the printing process. Figure 8: Three algorithms are used to extract the renderings of the feature points (**a**) Feature point extraction effect based on FFT-SIFT; (**b**) Feature point extraction effect based on the AKAZE; (**c**) Feature point extraction effect based on the FFT-SIFT-AKAZE algorithm. The Table 3 respectively records the average number of feature points extracted, feature points extraction time, feature points matching time and the number of final feature points matching based on the FFT-SIFT algorithm, AKAZE algorithm and FFT-SIFT-AKAZE algorithm in the actual printing process. Figure 10 shows the 3D reconstruction point  
Figure 9: Feature point matching results based on three algorithms. (**a**) Feature points matching result based on the FFT-SIFT algorithm; (**b**) feature points matching result based on the AKAZE algorithm; and (**c**) feature points matching result based on the FFT-SIFT-AKAZE algorithm. clouds of traditional SIFT matching algorithm and FFT-SIFT-AKAZE 3D reconstruction point clouds. The following conclusions can be drawn from the comparative analysis of the test results: Compared with FFT-SIFT algorithm alone, the fusion algorithm of FFT-SIFT-AKAZE increases the number of feature matches by 72.0%, which is far higher than the number of feature points extracted by the SIFT and AKAZE algorithms. Although the extraction and matching time of the algorithm in this paper is slightly increased, the matching results of the point cloud image obtained by the algorithm are more dense. The number of feature matching of edge contour increases significantly, and the details of contour become richer.