# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 4 Experiments
### Scalability Analysis for Real-World Applications  
As a highly effective technique for scene reconstruction, 3 DGS has great potential for various applications. If successfully implemented, it could have a significant impact in many fields, particularly in virtual reality (VR), augmented reality (AR), and digital scene modeling. However, many practical application scenarios, especially those involving resource-constrained platforms such as mobile devices (e.g., VR headsets), impose strict limitations on model size and computational resources. In these conditions, the large size and computational demands of the original 3 DGS models often make them unsuitable for direct use on such devices. Figure 12: ANOVA Comparison of Different Gaussian Compression Methods. To address this challenge, our proposed compression method effectively reduces the size of 3 DGS models while preserving as much of their original quality and detail as possible. This compression approach not only solves the problem of excessive model size but also ensures the broad application of 3 DGS technology on mobile devices and other resource-constrained platforms. It allows more users across various fields to benefit from this technology, promoting its adoption and practical use in real-world scenarios. To assess the scalability of practical applications, we employ training time, frames per second (FPS), compressed model size, and memory usage as key performance indicators for statistical analysis. The results for several sub-datasets from the Tanks & Temples and Mip-NeRF 360 datasets are presented in Table 8. The uncompressed model sizes range from 180 MB to 1430 MB; however, the training time for our model remains consistently within 40 min, with significant improvements observed in FPS. Notably, there appears to be a correlation between model training time and the initial size of the model. For instance, models with relatively large base sizes, such as the Bicycle and Garden datasets, exhibit a notable increase in training time. Nevertheless, this relationship is not absolute, as demonstrated by the stump dataset, where, despite the large base model size, the training time remains at a moderate level. Figure 13 visualizes the variation in memory usage during the training process. To investigate the performance trends associated with increasing dataset size or complexity, we selected the Lighthouse, Family, and Bicycle datasets, with approximate sizes of 200 MB, 500 MB, and 1500 MB, respectively, and conducted testing on an NVIDIA RTX 3090. Our experiments revealed that the memory usage during the vector quantization phase remains fixed across all three datasets, consistently at 16,054 MB (equivalent to 15.6 GB), and thus is not shown in the figure. Figure 13 presents only the memory usage during the Gaussian quantity compression module and the knowledge distillation module. The data indicates that there is no absolute linear relationship between model size and memory usage. From the trends observed in the line chart, memory usage fluctuates significantly between 15,000 and 30,000 iterations, peaking during the knowledge distillation phase. Based on our experimental findings, we conclude that our method requires at least 16 GB of GPU memory to run efficiently, while 25 GB of GPU memory is sufficient for the vast majority of cases.