## 3 Results
### Evaluation on 3D Reconstruction Precision and Performance  
From the computation theory of spatiotemporal correlation, it can be deduced that capturing more image pairs leads to higher accuracy. On the contrary, fewer image pairs reduce the capturing time and have a weak impact on the measurement accuracy of moving objects. Consequently, this study focuses on improving the measurement efficiency and optimising the compromise between the accuracy and speed of the algorithm. Several IR image pairs were projected with a rotary speckle projector using the experimental device shown in Figure 7. The number of speckle stereo image pairs, N, ranged from 1 to 12, and the resolution was 1280 \(\times\) 1024. The binocular infrared camera was calibrated using [PERSON]'s [38] method to obtain the camera parameters, with which the acquired IR images were rectified to facilitate correlation computation. To obtain a higher measurement accuracy, different combination parameters were used for different numbers of stereo image pairs. Different matching window sizes were adopted for image pairs, N, during coarse and fine matching. During coarse disparity calculation, usually, coarse window sizes, \(w_{c}\), ranged from 5 to 15, the grid interval was equal to the window sizes, coarse disparity ranged \(d_{c}^{\text{min}}\)\(=-200\), \(d_{c}^{\text{max}}=200\) and the shrunken search radius was \(r_{c}^{p}=w_{x}^{r}+2\) for disparity propagation. For fine disparity calculation, the disparity range, \(d_{f}^{\text{min}}\)\(=-w_{x}^{f}-1\), \(d_{f}^{\text{max}}=w_{x}^{f}+1\) and the search radius, \(d_{f}^{p}=(d_{f}^{\text{max}}-d_{f}^{\text{min}})/2+1\), were applied for the coarse disparities, and the fine matching window size, \(w_{f}\), which ranged from three to seven. Normally, the coarse matching window-size setting conditions are \(w_{c}=w_{f}+\Delta w,\ \Delta w=2\ or\ 4\). In our experiments, the matching threshold, \(\tau\)\(=0.3\), was set for the spatiotemporal correlation coefficients in order to discard unreliable matching. According to the coarse-to-fine spatiotemporal correlation method, the coarse matching window, \(w_{c}\), is only used to calculate the initial value of the disparity, and the final accuracy of the measured objects are determined by the fine matching window, \(w_{f}\). The cost of calculating the coarse disparity is small. Therefore, in the following description, the parameter, \(w_{f}\), which has the greater impact on reconstruction accuracy and time cost, is considered. When the matching window size needs to be selected, the larger window plays the role of a low-pass filter, which slows the steep changes of the surface of the measured objects, resulting in reduced spatial resolution or loss of high-frequency details. A smaller window can obtain richer detailed information on the surface of the object, but it also leads to an increase in the matching error rate. In this sense, the choice of matching window size is not absolute, and an appropriate window size must be investigated from a statistical perspective to balance the reliability of object reconstruction and spatial resolution. The 3D reconstruction results are shown in Figure 11, where the number of stereo image pairs, N, is combined with a fine window size, \(w_{f}\). Figure 10: Three-dimensional scanned data with ATOS Core 300; (**a**–**c**) are different perspectives of the 3D model. The impact of changes in the projected pattern number, N, and fine matching window, \(w_{f}\), was analysed on the accuracy of 3D reconstruction through a specific quantitative analysis. The average error and standard deviation were used as error evaluation criteria. The variation curves of the average error and standard deviation along with the change in N, \(w_{f}\), are shown in Figure 12. As shown, when N continues to increase, the measurement error decreases accordingly. Meanwhile, the optimal correlation window does not remain static but decreases as N increases.