# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 4 Experiments
### Performance Evaluation  
Table 1 reported the performance of the original 3 DGS model and several NeRF models, all of which were executed on an NVIDIA A6000 GPU. Since our experiments were conducted on an NVIDIA RTX 3090 GPU, we re-evaluated the performance of the 3 DGS model on the same GPU (denoted as 3 DGS *) to ensure a fair comparison. In the test results of existing work [7; 20; 21; 47], the commonly used Tanks & Temples datasets are partial sub-datasets, such as Train and Truck. Therefore, we only count the commonly used sub-datasets in Table 1, but in Table 2, we test more sub-datasets of Tanks & Temples to verify the performance of our model. Additionally, we compared our model with more advanced 3 DGS compression algorithms. From Table 1, it can be seen that while the rendering quality of our model is slightly lower than that of the advanced methods, it achieves the highest compression ratio. Model compression inherently leads to some degradation in reconstruction quality. However, considering the substantial compression achieved, these quality losses are acceptable within the scope of our research. The minor reduction in quality is a reasonable trade-off for the significant improvements in storage efficiency and computational resource savings. Table 2 presents the experimental results for each sub-dataset of the Tanks & Temples dataset. The findings indicate that the compressed Gaussian model experiences a decline in visual metrics, with the most significant reductions observed in PSNR and LPIPS. The PSNR decreased by a maximum of 2.5 points, and the maximum decrease in LPIPS was 0.12. SSIM shows a slight decrease. Despite the degradation in visual quality, the model's size is reduced by nearly 30 times on this dataset. In practical applications, the ease of deploying the model through compression often takes precedence overachieving high-precision reconstruction quality, particularly in scenarios with limited storage space and processing power. The balance between compression and quality retention is critical for the scalability and practicality of 3D scene reconstruction technologies in real-world settings. Table 3 presents the experimental performance of the Mip-NeRF360 dataset. Compared to the Tanks & Temples dataset, each sub-dataset of Mip-NeRF360 contains high-resolution images from a greater number of viewpoints, resulting in a larger overall dataset size. The inclusion of multiple viewpoints in this dataset facilitates improved model training. Despite the larger size of the dataset, our method achieves a higher compression ratio in terms of model size, and the decline in reconstruction accuracy is less pronounced than that observed with the Tanks & Temples dataset. This partially demonstrates the significant impact that the number of viewpoints has on the reconstruction accuracy of our model. Visual perception serves as a critical measure of reconstruction quality. Figure 4 presents a comparison of the original images, those rendered by the original 3 DGS model, and the renderings generated by our model. Our compressed model does not exhibit \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**Tanks \& Temples**} \\ \cline{2-5}
**Method** & **PSNR** & **SSIM** & **LPIPS** & **Size** \\ \hline Plenoxels [48] & 21.08 & 0.719 & 0.379 & 2.3 GB \\ INGP-base [8] & 21.72 & 0.723 & 0.330 & 13 MB \\ INGP-big [8] & 21.92 & 0.745 & 0.305 & 48 MB \\ Mip-NeRF360 [9] & 22.22 & 0.759 & 0.257 & 8.6 MB \\ SDCS & 23.14 & 0.841 & 0.183 & 411 MB \\ \hline
3 DGS \({}^{\star}\) & 26.34 & 0.902 & 0.148 & 302.25 MB \\ Lightgaussian [21] & 24.89 & 0.876 & 0.216 & 17.49 MB \\ Compressed3D [47] & 24.36 & 0.836 & 0.208 & 25.69 MB \\ C3 dgs [20] & 25.97 & 0.893 & 0.166 & 27.57 MB \\ Ours & 24.79 & 0.839 & 0.238 & **8.67 MB** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Qualitative results of the proposed method evaluated on Tanks & Temples datasets.