## 1 Introduction  
Multi-view 3D object reconstruction is an application of modern computer vision that has wide applications in many fields such as augmented reality, cultural heritage protection, autonomous driving, robotics, and medical imaging. The most important challenge in 3D reconstruction is the extraction of accurate and efficient features of an object from multiple views in order to create realistic and detailed models of objects with 3D representation [1]. Traditional reconstruction methods, such as SfM (Structure from Motion) [2] and SLAM (Simultaneous Localization and Mapping) [3], have shown difficulty in balancing accurate reconstruction, especially in real-time and large-scale applications. Nowadays, several deep learning methods have shifted towards recurrent neural networks (RNNs) [4, 5] and convolutional neural networks (CNNs) [6, 7, 8, 9, 10], which have demon strated significant success in feature extraction and spatial understanding of 3D reconstruction tasks. However, these methods experience difficulties in optimizing long-range dependencies while maintaining effective information exchange between multiple views during the encoding process. In contrast, the introduction of transformer-based [11; 12; 13; 14; 15] models has attracted attention for demonstrating the capability to capture long-range dependencies among relationships between input tokens, which makes these models particularly effective for visual understanding of spatial data across multiple views. Vision Transformers (ViTs) [16] are demonstrated as powerful tools for capturing global information through their attention mechanism, which splits images into fixed-size patches and analyzes them sequentially. When applied to multi-view 3D reconstruction tasks, ViTs face difficulties because of their fixed patch size along with performance inefficiencies as well as the heavy amount of view inputs [17]. Recently, swin transformers [18; 19; 20] have shown great promise in addressing many of these processing challenges by combining a hierarchical design with shifted window attention in the field of computer vision. Through their hierarchical design, swin transformers can efficiently process large-scale data while maintaining the ability to capture long-range dependencies. Unlike ViTs, swin transformers divide the image into local windows for attention, increasing the receptive field along the layer path, which makes them very well-suited for processing multi-view data in 3D reconstruction. Despite these advantages, it remains a challenge to effectively handle the intricacy of multi-view 3D data, especially when views increase and the model has to handle a large number of token interactions between views. To address this issue, the proposed WGA-SWIN-based 3D reconstruction method introduces a novel encoder framework, which integrates the Window Grouping Attention (WGA) mechanism into the swin transformer architecture. The WGA mechanism segments tokens from each window into groups based on semantic importance, allowing separate attention operations within each group. This effectively captures intra- and inter-view relationships, increasing efficiency while preserving a global feature representation. This strategy allows the model to focus on the most relevant features while maintaining a large representation. By encoding the WGA mechanism into the swin transformer architecture in the encoder, the proposed grouping technique evaluates the importance of tokens from each different window based on their attention value and their hierarchical design and shifts window attention to extract features from multiple views efficiently, thus extracting local and global dependencies for high-quality 3D reconstruction. For our task, we developed a progressive hierarchical decoder that integrates transposed convolution into the swin transformer blocks, which progressively up-sampled voxel representations to ensure high-resolution and detailed 3D reconstructions.