## 5 Parameter Optimization Method Based on DE Algorithm
### L-Shade
A series of experimental results show that compared with STZNCC, STMCF has advantages in accuracy, computation time and reconstruction quality. However, there are still several aspects which need to be discussed. 1. The error of \(d_{\text{A}}\), \(d_{B}\) and \(D\) shown in Tables 4 and 5 do not decrease with the increase of N, which is not consistent with the results presented in Figure 5:3 That is because the measured result of \(d_{\text{A}}\), \(d_{B}\) and \(D\) are obtained by least square sphere fitting algorithm; similar results have also been reported in the literature [24]. Nevertheless, the error statistics shown in Figure 7 are calculated by a comparison between the reconstruction result and ground truth. 2. According to the results of Figure 12, on the whole, the performance of STMCF is better than STZNCC in terms of computation time. However, it is easily observable that the growth rate of STMCF in computation time is higher than that of STZNCC. So, when the value of N is smaller, the advantage of STMCF in computation time is more obvious. 3. Since there is no suitable training data with ground truth, a training strategy is explored in this paper. Experimental results validate the effectiveness of the strategy. This shows that the training results acquired from passive stereo vision data can be applied to the stereo matching of speckle stereo image pairs when the image acquisition environment is similar. The above conclusion can be helpful for other relative research works. ## 7 Conclusions

A novel random speckle 3D reconstruction scheme is presented. In this scheme, STMCF is proposed for stereo matching of speckle stereo image pairs. Besides, to automatically determine the values of all parameters included in STMCF, a parameter optimization method based on DE algorithm is designed and a training strategy is explored in this method. Experimental results demonstrate that our scheme can achieve accurate and high-quality 3D reconstruction efficiently and the performance of STMCF outperforms the state-of-the-art method based on spatiotemporal correlation, which is widely used in random speckle 3D reconstruction. At present, STMCF cannot meet the requirements for real-time application. In future work, we intend to reduce the computation time by hardware acceleration and apply it to 3D reconstruction of the dynamic indoor scene. Conceptualization, L.K., W.X. and S.Y. ; methodology, L.K. and W.X. ; software, L.K. and W.X. ; validation, L.K. ; investigation, L.K. and W.X. ; resources, L.K. and W.X. ; data curation, L.K. ; writing--original draft preparation, L.K. ; writing--review and editing, L.K. and S.Y. ; supervision, S.Y. ; project administration, S.Y. ; funding acquisition, W.X. and S.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by Major Science and Technology Project of Sichuan Province grant number 2018 GZDZX0024, Science and Technology Planning Project of Sichuan Province, grant number 2020 YFG0288, Sichuan Province Key Program, grant number 2020 YFG0075 and Sichuan Province Science and Technology Program, grant number 2019 ZDZX0039. Not applicable. Data Availability Statement: The dataset used to support the findings of this study are included in the article, which are cited at relevant places within the text as [30]. We thank the anonymous reviewers for their insightful suggestions and recommendations, which led to the improvement of the presentation and content of the paper. The authors declare no conflict of interest. ## References

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