## 3 Results
### Evaluation on 3D Reconstruction Precision and Performance
Figure 13 shows the curves of the disparity computation time corresponding to different N values with the enlargement of the fine matching window,\(w_{f}\). Figure 11: Three-dimensional reconstruction results with a number of stereo image pairs, N, and a fine matching window, \(w_{f}\). The reconstruction accuracy continues to improve as N increases. When N is small (e.g., N \(\leq\) 3), a larger window creates a more precise and complete model. Regarding the choice of matching window size, a larger window image in the measurement of shape will function as a low-pass filter and suppress rapid variations in the measured field, leading to a reduction in spatial resolution or loss of high-frequency details, including an increase in computation cost. A comparison between the reconstruction results obtained by our proposed method and the scanned model using Core 300 is shown in Figure 14.  
Figure 15a-c shows the three-dimensional reconstruction results of different real faces when the speckle stereo image pair N = 12, and each group of results contains a comparison of untextured and textured maps. The 3D reconstruction results of the author's various expressions when N = 12 speckle stereo image pairing are shown in Figure 16. The contours of the lips, eyebrows, eye sockets and sides of the nose of the person are clearly displayed in the results. Based on the 3D face acquisition experimental platform and reconstruction method of rotary speckle projection, combined with Figures 10-13 and Table 1, it is found that the 3D reconstruction results have the following characteristics:  
1. In the current configuration environment, the reconstruction accuracy of the 3D model continues to improve as N increases the number of speckle stereo image pairs, but when the projected speckle patterns exceed a certain range (N \(\geq\) 6), the trend of accuracy improvement gradually weakens. 2. To obtain a higher 3D reconstruction accuracy, the optimal fine matching window is not as small as possible. For a given number of patterns, the optimal fine window size is determined for a given number of patterns. For example, when N = 1, 9 \(\times\) 9 is the optimal fine window size and the measurement error is the smallest; when N = 3, the optimal fine window size is 7 \(\times\) 7. When N = 12, the optimal fine window size  
Figure 12: Comparison of reconstruction error obtained between the projected pattern numbers, N, and fine matching window, \(w_{f}\). When N continues to increase, the reconstruction error decreases accordingly. Meanwhile, the optimal window size will also drop. Figure 13: Curves of computation time corresponding to the change of fine window size for different N.  
becomes 3 \(\times\) 3, the average error is 0.071 mm, and the standard deviation is 0.091 mm. From the overall trend, as the number of speckle stereo image pairs increase, the optimal matching window continues to shrink. 3. The computation time increases as the matching window increases. The greater the number of projected patterns involved in the calculation, the faster the computation time. As shown in Figure 10, in the case of a fixed number of speckle patterns, the computation time increases proportionally with the increase in the matching window. 4. There is a trade-off between measurement accuracy and calculation cost. When the control average error is less than 0.15 mm, the overall reconstruction error decreases with the increase in the number of projection patterns, and the optimal window size continues to shrink. Combining the comprehensive analysis of Figures 9 and 10, it is found that to obtain the best measurement accuracy for each number, N, of stereo image pairs, the computation time also increases slightly as the number of speckle patterns increases. Figure 14: (**a1–a3**) Three-dimensional (3D) reconstruction results of (N = 3, 6, 12) different speckle stereo image pairs. (**b1–b3**) Corresponding comparison maps with 3D scanned data by ATOS Core 300.