# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 4 Experiments
### More Technical Details  
The pseudocode of our work is shown as Figures 9 and 10. The attribute parameters of Gaussian points are updated during the training process. For example, parameters such as the scale and rotation of the Gaussian points are adjusted. When an area undergoes over-reconstruction or under-reconstruction, the Gaussian points are cloned or split. As the iterations progress, parameters like scale and rotation evolve toward fitting the scene. However, this approach tends to be slower and can lead to the generation of more Gaussian points, resulting in excessive local Gaussian point density. The opacity parameter, which directly affects the rendering of the Gaussian points, plays a crucial role in controlling their impact on the scene. Even if the Gaussian points are large, a low opacity can minimize their influence on the rendered scene. Pruning based on opacity is an effective and efficient control mechanism. Therefore, we chose to add the code for manipulating opacity into the program. By examining the 3 DGS model section in Figure 11, it is evident that, with the progression of training iterations, the visual metrics consistently improve and eventually  
Figure 9: Pseudocode of the overall pipeline. stabilize. During this process, the model size will initially grow rapidly and then stabilize. In comparison with our model compression method, the visual metrics show an overall positive trend, although the curve is less smooth than that of the 3 DGS method. It can be observed that because a contribution-based pruning occurred at the 20,000 th iteration, the PSNR metric temporarily halted its upward trend, and the other two metrics experienced slight declines. However, with further iterations, these metrics gradually optimized and stabilized. In addition, we can also find that at the 20,000 th iteration, the model size of our method is much smaller than that of the 3 DGS model. Finally, under the effect of contribution ratio pruning, the model is compressed to 1/4 of the original size. Table 7 provides a comparison of effect sizes for different Gaussian compression methods across four metrics: PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), LPIPS (Learned Perceptual Image Patch Similarity), and Size on the MIP-NeRF 360 datasets. The effect size is a measure that quantifies the magnitude of the difference between two groups. Cohen's d is a measure of effect size used to indicate the standardized  
Figure 10: Pseudocode of Gaussian quantity compression. difference between two means to quantify the magnitude of an effect independent of sample size. This makes it particularly useful for comparing results across different methods. In the context of comparing different Gaussian compression methods, a large Cohen's d effect size for size suggests a significant difference in the file sizes produced by the compared methods. Our method has a large positive effect size for file size compared to other methods (e.g., 3 DGS [7] vs. Ours: Size = 2.27), indicating that it produces significantly smaller file sizes, which is advantageous for storage and transmission efficiency. Figure 12 presents a comparative analysis of different Gaussian compression methods using ANOVA (Analysis of Variance). The box plots illustrate the performance metrics of four methods: 3 DGS [7], Lightgaussian [21], C3 dgs [20], and Ours. In the PSNR (Peak Signal-to-Noise Ratio) comparison, our method shows a slightly lower median value compared to the other three methods, indicating that it may have a higher noise level in the reconstructed images. However, the range of PSNR values for our method is relatively narrow, suggesting more consistent performance across different datasets. Lastly, in terms of size, since the compression rates of Lightgaussian [21], C3 dgs [20], and ours are all high, if the indicators of the 3 DGS method (average: 756 MB) are displayed in the figure, it will affect the visual display effects of the other three methods. Therefore, we only draw the visualization images of these three methods.