# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 2 Related Work
### Learning-Based Methods
#### 2.2.2 Point Cloud-Based Reconstruction  
In parallel, significant progress has been made in point cloud generation, offering a memory-efficient way to represent 3D structures through an unordered set of points (x, y, z coordinates). Unlike structured representations, i.e., meshes that store connectivity information between vertices and edges, point clouds capture geometric details without the need for maintaining connectivity, which simplifies the process while preserving essential shape information. Early approaches in this domain utilized deep encoder-decoder architectures to estimate 3D point clouds from single images using Chamfer and Earth Mover's distances as loss functions to assess geometric similarity and shape accuracy [14]. Building on this, 3D-LMNet introduced a method for knowledge transfer between 2D and 3D domains, particularly targeting single-view reconstruction tasks. It uses a point cloud autoencoder trained with Chamfer distance (CD) loss to map 2D images into a learned 3D latent space. An extension with variational autoencoder (VAE) further allowed for generating multiple plausible outputs by capturing uncertainties inherent in single-image reconstructions [29]. Apart from GANs, other conditional flow-based networks have also been studied to achieve precise computation and flexible manipulation in the latent space [30]. Another approach, DensePCR [31], adopts a strategy to hierarchically refine low-resolution predictions through the aggregation of local and global features. The single-encoder multiple-decoder (SE-MD) network [32] further advanced these techniques by employing a single autoencoder to learn feature distributions, coupled with multiple decoders to generate point clouds. These outputs were then fused to create detailed 3D reconstructions. It is similar to networks that utilize silhouettes as intermediate representations [33; 34], which are combined with image features to generate point clouds for handling occluded objects [34]. More recently in this domain, both single-stage and two-stage networks have improved the accuracy of point cloud generation [35; 36; 37; 38]. For instance, Pixel2 point [37] introduced a single-stage network that uses an initial sphere point cloud to efficiently learn geometric shape parameters. Similarly, 3D-ReConstnet [35] applied a residual network and multilayer perceptions (MLPs) to extract features and predict point sets, using a learned Gaussian distribution to refine occluded regions. This method was later enhanced by using Detnet architecture with Exponential Linear Unit (ELU) activation, merging Earth Mover's distance (EMD) and Chamfer distance (CD) losses into a unified loss function [38]. Subsequent advancements like 3D-CDRNet designed a two-stage point cloud reconstruction network that combines image features with a proposal retrieval branch. This model integrates an autoencoder, residual networks, and multilayer perceptrons (MLPs) to refine point set prediction, especially for occluded regions [36]. Although two-stage networks often outperform single-stage models, they tend to be computationally intensive and are limited to predefined categories. In contrast, single-stage networks are more efficient but often struggle to capture complex geometries due to the disparity between 2D image features and 3D representations. Considering their lightweight design and suitability for integration with IoT and real-time devices, our research focuses on improving single-stage networks. However, unlike existing methods that either directly convert image features into 3D representations or merge them with initial point clouds, our approach strategically fuses spatial image features with initial spherical point cloud features. This fusion normalizes feature values, improving the learning process by bridging the gap between images and their corresponding 3D models. Additionally, SS3 DNet-AF incorporates attention mechanisms to focus on relevant features while filtering out noise, enabling accurate capture of complex geometries and shapes.