# Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splitting
## 4 Experimental Evaluation
### Reconstruction Evaluation
The upward—pointing arrow (\(\uparrow\)) indicates that the larger the value of the evaluation metric, the better the effect. The downward—pointing arrow (\(\downarrow\)) indicates that the smaller the value of the evaluation metric, the better the effect. The bold—faced values are the optimal values for each corresponding evaluation metric, facilitating readers’ comparison. Same as below. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Image Reconstruction**} & \multicolumn{2}{c}{**Novel View Synthesis**} \\ \cline{2-5}  & **EmerNeRF** & **Ours** & **EmerNeRF** & **Ours** \\ \hline Seq105887... & 28.96 & 35.52 & 28.73 & 32.58 \\ Seq106250... & 28.35 & 35.28 & 27.59 & 32.36 \\ Seq110170... & 28.79 & 35.46 & 28.89 & 33.65 \\ Seq119178... & 27.98 & 34.12 & 27.51 & 32.49 \\ Seq122514... & 28.37 & 35.31 & 28.45 & 32.89 \\ Seq123392... & 28.68 & 35.44 & 28.75 & 33.24 \\ Seq148106... & **29.02** & **36.19** & **28.09** & **34.81** \\ Average & **28.59** & **35.33** & **28.29** & **33.15 Figure 6 presents qualitative comparison results of our method (Ours) with Mars and 3 DGS [8,31] on dynamic scenes from the Waymo dataset. In complex dynamic environments such as urban streets and highways, our method is capable of accurately reconstructing fine details of moving objects--for example, the text and structure on the orange sightseeing bus and the contours of vehicles on the road. In contrast, Mars and 3 DGS suffer from significant blurring and distortion, especially when handling fast-moving objects, with 3 DGS failing to recover the object appearance in many cases. Compared to the Ground Truth, our method produces images that are closer in visual quality and structural consistency. Figure 7 shows additional comparisons on the KITTI dataset, further demonstrating the robustness of our approach. In scenes with multiple moving vehicles, our method successfully reconstructs object poses and edge details, yielding sharp and natural results. In comparison, MARs exhibits evident motion blur and ghosting, while 3 DGS struggles to reconstruct fast-moving objects. These two sets of experiments consistently indicate that our method significantly outperforms state-of-the-art baselines in handling dynamic scenes, preserving and restoring complex motion-related details more effectively. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**Image Reconstruction**} \\ \cline{2-3}  & **StreetSurf** & **Ours** \\ \hline Seq100613... & 28.14 & 35.52 \\ Seq150623... & 28.58 & 35.28 \\ Seq158686... & 27.95 & 35.46 \\ Seq166085... & 27.68 & 34.12 \\ Seq322492... & 28.75 & 35.31 \\ Seq881121... & 28.62 & 35.44 \\ Seq938501... & **28.84** & **36.19** \\ Average & **28.59** & **35.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Static scene PSNR comparison sequence. Figure 6: Qualitative comparison results of dynamic scenes on the Waymo. In our dynamic sampling strategy, Gaussian points are dynamically distributed across feature map slices to capture both global and local dynamic appearance features. The number of feature map slices, denoted as k, influences the final dynamic appearance characteristics. To assess its effect, we controlled for other variables and performed experiments with varying k values through linear transformations. Figure 8 illustrates the impact of varying the number of dynamic feature maps k on model performance, evaluated using PSNR, SSIM, LPIPS, and FPS. As the number of feature maps k increases, both PSNR and SSIM peak at k = 4, indicating optimal image reconstruction accuracy and structural consistency. Meanwhile, the LPIPS value is relatively low at this point, reflecting better perceptual quality. However, FPS gradually decreases as the number of feature maps k increases, showing that more feature maps k introduce greater computational overhead and reduce real-time rendering speed.