## 6 Conclusions  
In this paper, we introduce ED\({}^{2}\)IF\({}^{2}\)-Net, the first single-view 3D reconstruction network based on the Pyramid Vision Transformer. Our network disentangles objects' implicit fields into deformed implicit fields and enhanced displacement fields. IFDBs refine the coarse implicit fields by analyzing pixel-aligned local features across scales, capturing finer topological structure details in the deformed implicit fields. Moreover, we enhance the displacement fields in both spatial and channel dimensions to preserve surface details. By employing a novel deformation loss and Laplacian loss, ED\({}^{2}\)IF\({}^{2}\)-Net achieves high-fidelity reconstruction, capturing both the structure and surface details of objects. On the ShapeNet dataset, ED\({}^{2}\)IF\({}^{2}\)-Net delivers superior performance, with ED\({}^{2}\)IF\({}^{2}\)-Net-L achieving the best mean IoU, CD, EMD, ECD-3D, and ECD-2D values of 61.1, 7.26, 2.51, 6.08, and 1.84, respectively. Compared to other methods, ED\({}^{2}\)IF\({}^{2}\)-Net excels in reconstructing finer topological structures while preserving enhanced surface details. It overcomes the limitations of alternative approaches that may compromise surface details or yield incorrect topology, resulting in higher-quality reconstructions. Our research represents a significant milestone in single-view implicit 3D reconstruction. We propose the first transformer-based single-view implicit 3D reconstruction network, opening up new possibilities for solving such tasks using transformers. ED\({}^{2}\)IF\({}^{2}\)-Net achieves state-of-the-art performance on the ShapeNet dataset while maintaining competitive inference time. The proposed IFDB and deformation loss can be readily applied to future works, enabling better reconstruction results in single-view implicit 3D reconstruction. The disentangled deformed implicit fields and enhanced displacement fields in our network benefit downstream applications, including surface detail transfer and pasting a logo. Furthermore, our framework can be optimized for embedded platforms, shedding new light on industrial applications such as VR/AR. Beyond real-time rendering challenges, the framework holds promise for industries such as robotics and autonomous driving. Conceptualization, [PERSON]; methodology, [PERSON] and X.Y. ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] and X.Y. ([PERSON]); formal analysis, [PERSON] ([PERSON]), [PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; investigation, [PERSON] ([PERSON]); resources, [PERSON]; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]), [PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; visualization, [PERSON] ([PERSON]); supervision, [PERSON]; project administration, D.Z. ; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The ShapeNet dataset and the Online Products dataset used in this study are available at [[https://shapenet.org](https://shapenet.org)]([https://shapenet.org](https://shapenet.org)) (accessed on 25 May 2023) and [[ftp://cs.stanford.edu/cs/cvgl/Stanford_Online_Products.zip](ftp://cs.stanford.edu/cs/cvgl/Stanford_Online_Products.zip)]([ftp://cs.stanford.edu/cs/cvgl/Stanford_Online_Products.zip](ftp://cs.stanford.edu/cs/cvgl/Stanford_Online_Products.zip)) (accessed on 25 May 2023). Our code is built on top of D\({}^{2}\)IM-Net and MDISN, and we are very grateful to their authors for their crucial support of the open source release of the code. The authors declare no conflict of interest. All the abbreviations in the manuscript and their respective explanations are as follows: