## 1 Introduction  
Single-view 3D reconstruction aims to reconstruct object shapes from single-view RGB images, which are widely used in areas such as robotics, VR, and autonomous driving. However, single-view 3D reconstruction faces challenges that stem from its intrinsic ill-posed nature. A large number of single-view 3D reconstruction approaches have been presentedrecently. Among them, deep-learning-based methods [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] have yielded more advanced results. Shape representations in deep-learning-based approaches can be classified into explicit [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] and implicit [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35] representations, and the latter ones are independent of memories and resolutions, which can significantly improve the reconstruction performance. The main ideas of the earlier data-driven implicit approaches [18; 19; 20; 21; 22] are to learn latent vectors of the input images and the neural networks are applied to fit the mapping relationship from the query points to a implicit scalar field. For example, DeepSDF [18] introduces latent codes that are able to represent similar objects and output signed distance functions (SDFs) approximating object shapes in combination with query point coordinates. IM-Net [19] encodes a single input image to extract a latent vector, which is then decoded together with the query point coordinates to generate an implicit scalar field value representing the spatial relationship between the point and the object shape. Occupancy Network [20] encodes different types of inputs into embeddings while converting query points into point features, and the decoder incorporates all the information and outputs a real number to indicate the occupancy probability of the query point. [PERSON] et al. [21] use the encoding vector of the input image as the weight matrix in an MLP for binary classification of query points, resulting in the generation of an implicit field. These methods can only reconstruct the coarse shape but fail to reproduce the details of the object. Rather than predicting a single global implicit field, PQ-NET [23] outputs a SDF for each intrinsic structure of the object and fuses these implicit fields to generate the final SDF, producing more promising reconstruction results. Recently, several novel CNN-based models have been proposed [24; 25; 26; 27; 28; 29]. DISN [24] fuses global and local features of the input image with point features of query points to obtain the fused SDF. MDISN [25] deforms a randomly generated SDF based on local feature variations at the layer level to approximate the ground-truth SDF. Ladybird [26] considers pixel-aligned local features of query points and their symmetry points, combining them with global features to output the SDF. Ray-ONet [27] integrates global features, local features, and scaling parameters to estimate the occupancy probability of spatial query points along rays, reducing complexity and improving performance compared to Occupancy Network [20]. [PERSON] et al. [28] merge global and local features extracted by an encoder, incorporate query points via linear interpolation, and use subsequent networks to predict occupancy values. In contrast to earlier works [18; 19; 20; 21; 22; 23], such methods [24; 25; 26; 27; 28] can better capture structure details and recover finer shapes owing to the integration of local features. However, details at the surface level such as depth, which are equally critical for visual perception, are still poorly reconstructed. D\({}^{2}\)IM-Net [29] focuses on recovering surface details, which often produces promising surface features, yet is unable to reconstruct the correct topological structures. Furthermore, previous CNN-based approaches [24; 25; 26; 27; 28; 29] often encounter two inherent limitations associated with the convolutional layer. Firstly, convolutional kernels treat all pixels equally, resulting in inefficiency when processing images.