# 3D Reconstruction Based on Iterative Optimization of Moving Least-Squares Function
## 4 Experiments
### Result  
To verify the effectiveness of this method, comparative experiments were conducted with the NP [37], OSP [38], and DeepMLS methods. In Figure 4, the surface generation of sofas and chairs demonstrates that the NP method causes the anomaly of surface loss. When dealing with airplane classes, airplane surfaces conjoin in unintended areas. In the surface generation of the sofa, the OSP method results in cushions incorrectly linked in certain areas, and the vehicle lacks detail, causing a loss of shape details and feature information. The modeling effect of DeepMLS in the table shape is finer than that of NP and OSP, but there are abnormal holes in the surface generation of the chair, resulting in discontinuity of the surface. In contrast, the method proposed in the paper avoids the aforementioned problems. Not only is there no adhesion on the sofa, but also the surface generation results of the chair show that the proposed method can effectively model the texture details of the chair rest. Through the visualization results in Figure 4, the proposed method has certain advantages over other deep-learning methods, showing better modeling accuracy and surface continuity, which brings breakthroughs and improvements to the object shape generation task. Several deep-learning-based surface-generation methods (DeepSDF, MeshP [39], LIG [40], IMNET [41], NP, DeepMLS) were quantitatively compared on the ShapeNet dataset. (\(\uparrow\) and \(\downarrow\) indicate that either larger data values are better or smaller data values are better, respectively). Table 1 shows the comparison results of the above methods on NC evaluation indexes. The comparative experimental results show that the proposed  
Figure 4: Visualization and comparison results of 3D surface-generation methods under the ShapeNet dataset. (The red frames display the local comparison results of the shape). method achieves a high score in the NC evaluation index, which means that the surface generated by the proposed method is closer to the ground truth surface in terms of accuracy and fidelity. Compared with other methods, this method can better restore the details and overall characteristics of the target shape to provide more accurate and reliable surface-generation results. Table 2 shows the comparison results of various methods on F-Score evaluation indicators. After data analysis, our method shows good quantitative results in various shape categories. Especially in the two categories of Display and Airplane, the method achieved the best score of 0.998. These results demonstrate the excellent performance of the proposed method in surface-generation tasks and the advantages of different shape classes. Table 3 shows the results of a comparison of the CD2 indicator between the various methods. Through data analysis, the proposed method performs well in multiple shape categories. Although it is inferior to DeepMLS in vessel and sofa categories, it is better than other methods in other categories and the mean of all categories, indicating that the proposed method has good performance in the surface generation task under the evaluation of CD2 index and has good effect and superior performance. To verify the effectiveness of the proposed method, comparative experiments are carried out on the ABC dataset and the Famous dataset, and the specific results are shown in Figures 5 and 6. By comparing the performance of each method on the ABC dataset, it can be observed that the proposed method can generate continuous surfaces and avoid abnormal surface phenomena such as shape \"hyperplasia\", adhesion, and \"ectopia\". This shows that the proposed method can maintain stable and excellent performance in processing different datasets.