# Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splitting
## 5 Conclusions  
In this study, a method was proposed for the unbounded dynamic 3D scenes that autonomous driving cars encounter. This method innovatively utilized the 3D Gaussian splitting technique and introduces a deep learning network on this basis. Through LiDAR-SfM point cloud fusion, the Gaussian color feature prediction network, and the pose-tracking mechanism, certain achievements were in autonomous driving scene reconstruction. Experimental results showed that this method performs well in key metrics. For example, in metrics such as PSNR, it approaches the baseline method using ground-truth poses, validating the effectiveness of modules like the pose-tracking mechanism.  
However, this research has certain limitations. Firstly, the current method relies on the precise spatio-temporal synchronization of LiDAR and cameras. In monocular or low  
Figure 10: Editing operations on the Waymo dataset.  
Figure 9: Ablation studies by visualization.  
frame-rate sensor scenarios, due to the lack of sufficient depth information and continuous observations in the time dimension, the performance may decline. Secondly, the pose initialization of dynamic objects still requires manual intervention and has not achieved full automation, which will increase labor and time costs in large-scale data processing and practical applications.  
Based on these results, future research can be carried out in the following directions. On the one hand, there are plans to expand the Gaussian model to multi-modal data, integrating data from more sensors such as IMUs (Inertial Measurement Units) and millimeter-wave radars, so as to enhance the robustness in complex environments (such as extreme weather and heavily occluded scenes). On the other hand, exploring lightweight network design, through techniques such as model compression and pruning, may reduce computational complexity and support real-time deployment on edge devices, thus promoting the widespread use of autonomous driving simulators in practical application scenarios.  
To promote reproducibility and encourage future research, we have publicly released the source code and pretrained models at: [[https://github.com/zhouyue270/Gaussian-UDSR](https://github.com/zhouyue270/Gaussian-UDSR)]([https://github.com/zhouyue270/Gaussian-UDSR](https://github.com/zhouyue270/Gaussian-UDSR)), accessed on 1 June 2025.  
Conceptualization, Y.S. and Y.Z. ([PERSON]); methodology, Y.Z. ([PERSON]); software, Y.Z. ([PERSON]); validation, Y.Z. ([PERSON]); formal analysis, Y.Z. ([PERSON]); investigation, Y.Z. ([PERSON]); resources, Y.S., B.T. and H.W.; data curation, Y.Z. ([PERSON]) and S.W.; writing--original draft preparation, Y.Z. ([PERSON]); writing--review and editing, Y.S. and Y.Z. ([PERSON]); visualization, Y.Z. ([PERSON]); supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
Research on Key Technologies of Intelligent Equipment for Mine Powered by Pure Clean Energy, Natural Science Foundation of Hebei Province, F2021402011. High-performance autonomous learning control theory for servo drive systems based on cooperative estimation of heterogeneous approximators, National Natural Science Foundation of China, 52465059.  
Dataset used in this research is available online.  
Author [PERSON] was employed by the company Jizhong Energy Fengfeng Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.