# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 1 Introduction  
In recent years, significant breakthroughs have been made in 3D scene reconstruction techniques, particularly in the area of radiance fields, with Neural Radiance Field (NeRF) [1] being a pivotal development. NeRF implicitly represents and learns the radiance field of a 3D scene through a multilayer perceptron (MLP), achieving high-quality reconstruction by utilizing images captured from multiple viewpoints. Compared to other traditional 3D scene reconstruction methods, the high-quality images rendered by NeRF highlight the advantages of differentiable radiance fields in scene representation. However, NeRF's slow training and rendering speed have become a significant bottleneck in the practical application of radiance field models. Although various methods [2, 3, 4, 5, 6] have been continuously proposed for accelerating the training and rendering process, they often come at the cost of depleting the reconstruction accuracy. With advancements in 3D reconstruction technology, the 3D Gaussian Splatting model (3 DGS) [7] has been proposed. Unlike discrete point cloud models, the 3 DGS model is differentiable across the scene due to the properties of 3D Gaussian points, allowing for higher-quality rendering after scene construction. Compared to NeRF, the 3 DGS modelavoids unnecessary computations in empty space and leverages rasterization techniques to quickly project and render 3D Gaussians onto a 2D image, significantly enhancing training and rendering speed. Despite using a completely different data structure, the 3 DGS model retains the properties of a differentiable radiance field, allowing for high-quality scene rendering akin to NeRF. In the realm of Neural Radiance Fields (NeRF) methods, InstantNGP [8] achieves significantly high training speed, while MIP-NeRF 360 [9] attains outstanding scene reconstruction accuracy. However, as an innovative scene representation method, the 3 DGS model not only rivals the state-of-the-art NeRF methods in terms of speed and accuracy but also supports near real-time rendering. 3 DGS has seen significant advancements across various research domains. The 3D Gaussian Splatting model has broad applicability across multiple domains, including Simultaneous Localization and Mapping (SLAM) [10; 11], dynamic scene reconstruction [12; 13; 14], Artificial Intelligence-Generated Content (AIGC) [15], autonomous driving [16], endoscopic scene reconstruction [17], and largescale scene modeling [18]. Due to its excellent performance and extensibility, 3D Gaussian Splatting modeling has quickly become a prominent branch in 3D reconstruction. However, the 3D Gaussian Splatting method is not without flaws. Its model parameter count is significantly larger than that of NeRF, which allows for high-quality reconstruction but at the expense of storage requirements. This presents a considerable limitation for resource-constrained applications such as AR/VR headsets and IoT devices. Therefore, it is crucial to investigate methods for compressing 3 DGS models to reduce storage requirements while maintaining rendering quality. Before conducting compression studies, it is essential to understand the model's storage format. 3 DGS models primarily represent scenes explicitly by training a large number of parameterized 3D Gaussian points. Each Gaussian point consists of 59 parameters, including such as position, rotation quaternion, covariance matrix, opacity, and spherical harmonic coefficients. During model training, the 3 DGS model first performs Structure from Motion (SfM) [19] based on training datasets to estimate camera positions and the initial point cloud, initializing a small number of 3D Gaussians based on the sparse point cloud. Throughout training, the original Gaussian points continuously clones and splits to generate new Gaussian points with numerous attribute parameters, updating these parameters to optimize the model's fit to the scene. However, this process inevitably generates redundant Gaussians.