# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 5 Conclusions  
We propose a method to highly compress 3D Gaussian models by a factor of 20-40. To reduce the number of Gaussian points, we combine proportional contribution pruning with learnable masks to prune away redundant Gaussian points. In the process, we demonstrate the theoretical and compression advantages of an integrated opacity update strategy. To address the problem of spherical harmonic coefficients taking up too much storage space, we use a teacher-student model framework and vector quantization to further compress the color representation parameters of the model. Experiments show that although the quality of the reconstructed scene is slightly reduced, our compression technique significantly  
Figure 13: Line chart of memory usage during training of some datasets.  
reduces the model size and greatly reduces storage requirements. This work helps to promote the development and application of 3D scene reconstruction technology.  
In our comparative experiments using the Tanks & Temples dataset, the compression ratios achieved by LightGaussian, Compressed3D, and Compact3 DGS are 17\(\times\), 12\(\times\), and 11\(\times\), respectively. While these methods all achieve a certain degree of efficient compression, our approach surpasses them with a compression ratio of 35\(\times\). Although the reconstruction quality is slightly compromised, the compression ratio is significantly higher than that of the other advanced methods. Through extensive experimentation, our model achieves a maximum compression ratio of 60\(\times\), with a minimum compression ratio consistently maintained at 20\(\times\), and the overall compression effect falls within the range of 20\(\times\) to 40\(\times\).  
Building on the contributions of this study, one promising direction for future research is the adaptation of the proposed high-fold Gaussian compression method to dynamic scenes. For instance, integrating motion-aware strategies, such as 4D Gaussian Splatting, could enable real-time rendering and reconstruction of dynamic scenes with high fidelity. However, addressing the challenges of occlusion and depth variation in dynamic scenes may require innovations in the pruning and compression processes. Adapting opacity optimization and contribution-based pruning methods to accommodate temporal dynamics could further enhance the applicability of the method in real-world scenarios.  
Conceptualization, S.Q., C.W. and Z.W.; Methodology, S.Q.; Validation, S.Q.; Investigation, S.T.; Resources, C.W.; Writing--original draft, S.Q.; Writing--review and editing, C.W., Z.W. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shandong Province (grant number ZR2024 MF145), the National Natural Science Foundation of China (grant number 62072469), and the Qingdao Natural Science Foundation (grant number 23-2-1-162-zygd-jch).  
Not applicable.  
The data and source are available from the authors upon request.  
The research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.