## 4 Experiment Results and Discussion
### Comparison with SOTA Approaches  
Our comparison concentrates on the implicit models that have achieved state-of-the-art results to date, mainly including IM-Net [19], MDISN [25], DISN [24], D\({}^{2}\)IM-Net [29], and its variant D\({}^{2}\)IM-Net\({}_{GL}\). IM-Net is similar to the coarse shape decoder of ED\({}^{2}\)IF\({}^{2}\)-Net, and MDISN and D\({}^{2}\)IM-Net are the corresponding baselines for the deformation decoder and the surface detail decoder, respectively. Moreover, DISN is by far the most excellent single-view implicit 3D reconstruction method with respect to geometric details. For comparative fairness, the above networks were all trained and tested based on the same benchmarks. Table 1 presents the quantitative comparison of all the aforementioned methods on ShapeNet. The results indicate that ED\({}^{2}\)IF\({}^{2}\)-Net-T and ED\({}^{2}\)IF\({}^{2}\)-Net-L outperform other methods in most object categories, demonstrating significantly higher mean values for each evaluation metric across 13 object categories compared to the other methods. Notably, ED\({}^{2}\)IF\({}^{2}\)-Net-L achieves state-of-the-art quantitative results on ShapeNet, with an IoU of 61.1, CD of 7.26, EMD of 2.51, ECD-3D of 6.08, and ECD-2D of 1.84. When compared to DISN, ED\({}^{2}\)IF\({}^{2}\)-Net-L exhibits a 7% increase in mean IoU, and a 34%, 12%, 12%, and 26% decrease in mean CD, EMD, ECD-3D, and ECD-2D, respectively. These quantitative results demonstrate that both ED\({}^{2}\)IF\({}^{2}\)-Net-T and ED\({}^{2}\)IF\({}^{2}\)-Net-L excel not only in overall shape (topological structure) but also in recovering edge details (surface details). It is important to note that ED\({}^{2}\)IF\({}^{2}\)-Net may not achieve the best performance in every category, which could be attributed to the the network being trained on all categories of ShapeNet. The network's sensitivity to the quantity and diversity of models within a single category may result in slightly inferior reconstruction results for categories with fewer models or predominantly similar models, such as phones. The qualitative comparison of different methods is presented in Figure 6. From the figure, it is evident that IM-Net can only reconstruct the coarse shape of the object, resulting in a loss of significant topological structure details (such as holes of the sofa backrest and handles of the table drawers) as well as surface details (e.g., chair backrest). Compared to IM-Net, DISN performs better in rebuilding topological structures and surface details, although the results may contain geometric noise leading to blurry surfaces (e.g., sofa backrest surface). However, DISN struggles in recovering details at small scales (bottom and backrest of the chair). While MDISN can reconstruct more detailed topological structures, it fails to recover surface details and even introduces shape distortions to the object (e.g., speaker and table). On the other hand, D\({}^{2}\)IM-Net shows promise in surface detail recovery but often produces incorrect topological structures for highly curved shapes (e.g., armrests and bottom of the chair) and introduces numerous artifacts (such as the table). In contrast, both ED\({}^{2}\)IF\({}^{2}\)-Net-T and ED\({}^{2}\)IF\({}^{2}\)-Net-L are able to reconstruct more visually appealing qualitative results. These methods enable the reconstruction of more complex topologies (e.g., holes in sofa backrests, handles of table drawers) and capture finer small-scale surface details (e.g., chair backrests). These findings align with the quantitative comparison in Table 1 and validate that ED\({}^{2}\)IF\({}^{2}\)-Net can effectively generate high-fidelity 3D shapes with accurate topological structure and surface details.