## 1 Introduction
This uniform treatment fails to capture the varying importance and dependencies of different pixels within an image. Secondly, due to the local nature of convolution, long-range pixel relationships are not effectively modeled. As a result, crucial contextual information may be overlooked, hindering the ability to fully understand and exploit the complex dependencies and interactions between pixels across the entire image. The PIFu series [30; 31; 32] incorporates pixel-aligned local features and depth information into their paradigm, with a primary focus on human reconstruction. With the advent of the works like ViT [36] and DeiT [37], transformers have obtained considerable attention in computer vision recently. Transformer-based vision models have achieved state-of-the-art performance in several downstream tasks, such as DETR [38] for object detection, SwinIR [39] for image restoration, Segmenter [40] for semantic segmentation, and MViTv2 [41] for image classification. While transformers have demonstrated preliminary success in many tasks including explicit 3D reconstruction [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35], whether they could be successfully employed to improve implicit 3D reconstruction is still unknown. To address the limitations of existing implicit methods that struggle to simultaneously reconstruct the topological structure and surface details of objects, ED\({}^{2}\)IF\({}^{2}\)-Net is proposed in this paper. Our approach utilizes transformers, specifically Pyramid Vision Transformer (PVT), to enable end-to-end single-view implicit 3D reconstruction. By leveraging PVT, we aim to mitigate the negative impacts of underlying convolutional layers in CNN-based methods, allowing for comprehensive reconstruction of both topological structures and surface details from a single image. For an input image, local features and a global vector are extracted using a pre-trained Pyramid Vision Transformer encoder [(43)]. Subsequently, a coarse shape decoder reconstructs a coarse implicit field based on the global vector. A deformation decoder, incorporating symmetry priors that provide extra knowledge about the object shape, predicts a deformed implicit field with finer-grained structure details using pixel-aligned local features and multiple implicit field deformation blocks (IFDBs). Finally, a surface detail decoder equipped with hybrid attention modules [(44)] (HAMs) constructs an enhanced displacement field, enabling the recovery of enhanced surface details from the local features. In order to facilitate the learning of the implicit field deformation function, IFDB offers a lightweight and effective approach. It refines the coarse implicit field by leveraging information from query points and pixel-aligned local features at neighboring scales. Through simple iterations, multiple IFDBs efficiently fit the deformation function, enabling the generation of the deformed implicit field that captures the finer topological structure of the object. In contrast to CBAM [(45)], HAM is a more novel and parameter-efficient module that significantly improves surface detail recovery performance. The output of the proposed ED\({}^{2}\)IF\({}^{2}\)-Net is a fusion of the deformed implicit field and the enhanced displacement field together. The main contributions of this paper include:  
1. A Pyramid-Vision-Transformer-based ED\({}^{2}\)IF\({}^{2}\)-Net is proposed for end-to-end single-view implicit 3D reconstruction, which disentangles implicit field reconstruction into accurate topological structures and enhanced surface details with competitive inference time. To our knowledge, it is the first method to utilize transformers for single-view implicit 3D reconstruction. Experimental results show superior performance in both overall reconstruction and detail recovery. 2. The finer topological structural details of the object are achieved through iterative refinement of the coarse implicit field using multiple IFDBs.