# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 4 Experiments
### Ablation Study  
#### 4.3.1 Experiments of Main Process Module  
To verify the effectiveness of each module, we conducted ablation experiments on four subsets of the Tanks & Temples dataset, with the average value of the results presented in Table 4. Red stands for the Gaussian point number reduction module, which includes the opacity update strategy and proportional contribution pruning with learnable masks. KD  
Figure 5: Visual comparison results of the original image, LightGaussian rendering, and our model rendering.  
Figure 6: Visualize the result of reducing the number of Gaussian points. We degrade the Gaussian points and visualize only the center point. It can be seen that our method effectively reduces the number of Gaussian points.  
represents the knowledge distillation module for reducing spherical harmonic coefficients, and VQ is the vector quantization module for compressing spherical harmonic coefficients. From the ablation experiments, we found that the red module, which reduces the number of Gaussian points, resulted in the largest decrease in metrics, especially in PSNR and LPIPS. Although this module caused the most significant drop in metrics, it also provided the most substantial compression, reducing the model size by nearly seven times compared to the baseline. The VQ module, representing vector quantization, also achieved notable compression while causing minimal visual metric decline, making it a highly effective step. By applying these modules cumulatively, our compression method achieves an extremely high compression ratio for the 3 DGS model.  
Figure 7 is a visualization of the ablation experiment of the main process modules. The dataset used is the Horse sub-dataset of Tanks & Temples. We use red boxes to frame two local areas on the complete original image and zoom in on the two areas in the renderings at different stages of the training process. We use the size of the 3 DGS model as a benchmark. Our method compresses to 65% at the 15,000 th iteration, 25% at the 30,000 th iteration, 15% after knowledge distillation, and 5% after vector quantization. From the first group of images, we can see that after the model is compressed when rendering areas with dense changes such as leaves, the edges have obvious degradation. We further zoom in on the image, and from the second group of images, we can see that obvious degradation has occurred in the high-frequency texture area of the leaves. In addition, we can find that in the first 30,000 iterations, the main changes occur in the morphology of the Gaussian points. In the knowledge distillation and vector quantization stages, the main changes occur in the color attributes of the Gaussian points.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Method\(\backslash\)Dataset** & & & **Tanks \& Temples** & \\ \hline
**Red** & **KD** & **VQ** & **PSNR** & **SSIM** & **LPIPS** & **Size** \\ \hline  & 3 DGS & & 26.345 & 0.902 & 0.148 & 302.25M \\ ✓ & & & 25.372 & 0.851 & 0.215 & 43.97M \\ ✓ & ✓ & & 25.082 & 0.845 & 0.223 & 28.98M \\ ✓ & ✓ & ✓ & 24.785 & 0.839 & 0.238 & 8.67 MB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment of main process module on Tanks & Temples dataset. Red denotes the Gaussian point number reduction module, KD denotes the knowledge distillation module for reducing spherical harmonic coefficients, and VQ denotes the vector quantization module.  
Figure 7: _Cont._