# Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splitting
## 4 Experimental Evaluation
### Reconstruction Evaluation  
Baseline Methods. We evaluated our method along with recent methods with Street-Surf [5], Mars [8], 3 DGS [31], SUDS [45], EmerNeRF [46], and PVG [47]. Table 2 compares our method with the baseline method regarding rendering quality and speed. We use PSNR, SSIM, and LPIPS [48] as metrics for evaluating rendering quality. Our method achieves the best overall performance across all metrics. Specifically, Gaussian-UDSR attains real-time rendering speeds of 128 FPS on Waymo and 136 FPS on KITTI, significantly outperforming most learning-based methods such as Mars, EmerNeRF, and SUDS, which operate below 0.1 FPS and are impractical for real-time deployment. While 3 DGS and PVG also support fast rendering, their reconstruction quality is substantially lower than ours. Our approach achieves the highest PSNR (36.43 on Waymo and 35.63 on KITTI) and SSIM (0.971 and 0.964, respectively), indicating superior fidelity and structural accuracy. Furthermore, we obtain the lowest LPIPS scores (0.047 on Waymo and 0.013 on KITTI), demonstrating that our reconstructions are the most perceptually faithful to the ground truth. For all the metrics, our model achieves the best performance among all the methods with an 8.8% improvement in PSNR, 75% reduction in LPIPS, and four orders ofmagnitude improvement in rendering speed over the Nerf-based methods [8; 46], which completed the whole training process in about one hour. Although 3 DGS renders faster than us, it can only be applied to static scenes, and the rendering effect under dynamic scenes decreases significantly. These results validate that Gaussian-UDSR not only provides high-quality rendering but also enables real-time performance, making it particularly well-suited for dynamic scene reconstruction in autonomous driving applications. We also selected EmerNeRF and StreetSurf for PSNR comparison of dynamic and static scenes respectively, as shown in Tables 3 and 4. We conducted a comprehensive comparison between our Gaussian-UDSR method and two state-of-the-art approaches, EmerNeRF and StreetSurf, on the tasks of image reconstruction and novel view synthesis. The results clearly demonstrate the superior performance of our method. Compared with EmerNeRF across seven sequences, our method achieves a significantly higher average PSNR of 35.33 vs. 28.59 in image reconstruction, and 33.15 vs. 28.29 in novel view synthesis, indicating improvements of 6.74 dB and 4.86 dB, respectively. Similarly, when compared with StreetSurf on another set of seven sequences, our method achieves the same average PSNR of 35.33, while StreetSurf only reaches 28.59, again showing a notable improvement of 6.74 dB. These consistent gains highlight the effectiveness of our 3D Gaussian-based representation and dynamic feature modeling in both preserving image fidelity and synthesizing novel views, even in challenging dynamic and unbounded environments. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Waymo Open Dataset**} & \multicolumn{3}{c}{**KITTI**} \\ \hline
**Methods** & **FPS \(\uparrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** & **FPS \(\uparrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** \\ \hline StreetSurf [5] & 0.081 & 27.58 & 0.876 & 0.340 & 0.35 & 25.02 & 0.725 & 0.251 \\ Mars [8] & 0.027 & 22.36 & 0.675 & 0.399 & 0.032 & 27.16 & 0.864 & 0.227 \\
3 DGS [31] & **177** & 27.14 & 0.896 & 0.227 & **209** & 22.58 & 0.828 & 0.311 \\ SUDS [45] & 0.026 & 29.08 & 0.849 & 0.236 & 0.050 & 28.43 & 0.876 & 0.138 \\ EmerNeRF [46] & 0.037 & 28.92 & 0.830 & 0.355 & 0.094 & 26.74 & 0.753 & 0.206 \\ PVG [47] & 38.67 & 33.49 & 0.954 & 0.189 & 42.53 & 33.76 & 0.961 & 0.155 \\ Ours & 128 & **36.43** & **0.971** & **0.047** & 136 & **35.63** & **0.964** & **0.013** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results on the dynamic scenes on Waymo and KITTI.