# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 1 Introduction  
Image-based 3D reconstruction has become essential across various industries, providing a cost-effective alternative to expensive equipment such as laser scanners and depth sensors. While 3D scanners and RGB-D cameras (depth sensors) are capable of capturing detailed 3D information, they come with significant limitations. These devices are often expensive, large in size, and require controlled environments or specific setups for accurate results. Additionally, they might not be practical for large-scale deployment, outdoor scenarios, or applications with strict budget constraints. Alternatively, simple image-capturing devices, such as standard cameras, are widely accessible and affordable, inspiring researchers to develop methods for reconstructing 3D shapes directly from images captured using these devices. While humans can naturally perceive the shape and structure of objects, replicating this ability in machines remains a significant research challenge. The applications of 3D reconstruction span diverse fields, including 3D character modeling, autonomous navigation for vehicles and robots for interacting with their surroundings, image-assisted surgical procedures where precise body scans are required, and 3D visualization of buildings and maps [1; 2; 3; 4; 5]. Despite the importance of 3D models, most imaging devices are still limited to capturing only two-dimensional (2D) information (xand y coordinates), lacking the depth (z-axis) information essential for creating accurate 3D representations. Typically, 3D models are represented as voxel grids, point clouds, or meshes. However, manually creating these models is time-consuming and expensive, which promotes researchers to develop automated tools for generating 3D models directly from 2D images. Figure 1 illustrates a 2D image and its corresponding 3D model from various angles. When a camera captures an image, depth information is lost due to the projection of a 3D scene onto a 2D plane [6]. To address this issue, several methods have been developed to recover the missing dimension to accurately reconstruct 3D models--a critical task, as no direct cues about the lost dimension are available in the image plane. Broadly, 3D reconstruction methods that create 3D models directly from 2D images can be classified into conventional and learning-based methods. These methods are further categorized based on their applications, ranging from general object reconstruction to specialized domains like 3D facial modeling for gesture recognition, 3D human body reconstruction, character modeling, and medical imaging (see Figure 2). Key studies provide valuable insights into each of these research areas. Traditional multi-view geometry methods, such as stereo vision and structure-from-motion, depend on capturing multiple images from different angles [7; 8], a process that is both time-intensive and impractical for large-scale applications [1]. These methods also require significant expertise and extensive object coverage, limiting their accessibility. Alternatively, single-view learning-based approaches are categorized into two branches. The first involves generating intermediate 3D proposals by searching for structurally similar objects from extensive datasets, followed by fusion and refinement based on coarse estimates. Although this approach is effective, it is computationally expensive due to high-dimensional feature matching and a large search space, which makes it unsuitable for real-time applications. Additionally, it restricts models to predefined object categories, reducing adaptability. The second approach learns 3D shapes directly from image features, without involving intermediate steps and initial proposals, but it often struggles to capture intricate geometries due to significant feature disparities between images and their corresponding 3D representations. To overcome these limitations, this paper introduces SS3 DNet-AF, a novel single-stage, single-view 3D reconstruction network.