# Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splitting
## 4 Experimental Evaluation
### Ablation Experiment  
In this section, we conducted ablation studies to evaluate the individual contributions of key components within our proposed method. In particular, we analyzed the impact of LiDAR depth, SfM geometry, their fusion module, the feature prediction network, and the pose-tracking mechanism. To validate the effectiveness of each module, we selected eight sequential scenarios from the Waymo dataset, covering diverse conditions such as  
Figure 8: Quantitative results of different K values on rendering quality. Figure 7: Qualitative comparison results of dynamic scenes on the KITTI. rainy and foggy weather, high traffic with many moving objects, sunny days, and cloudy weather. These experiments allowed us to assess the robustness and generalization of each component under various dynamic and challenging environments. Table 5 presents the quantitative results of ablation studies, evaluating the impact of removing key components from our method. Removing the LiDAR depth input (\"w/o lidar depth\") slightly decreases PSNR to 36.22 and increases LPIPS to 0.050, indicating that LiDAR's precise geometry is crucial for fine-grained depth accuracy, though the overall structure remains robust. Omitting the SfM input (\"w/o SfM\") leads to a more pronounced degradation, with PSNR dropping to 34.65, SSIM decreasing to 0.959, and LPIPS rising to 0.059. This shows that the camera-based geometric cues provided by SfM are essential for enhancing structural consistency and compensating for LiDAR sparsity, especially in distant or texture-poor regions. Omitting the feature prediction module (\"w/o Feature prediction\") also leads to significant degradation: PSNR drops to 34.91, SSIM falls to 0.962, and LPIPS rises to 0.056. This confirms that the feature prediction network is vital for capturing dynamic appearance variations under changing lighting, as its absence causes color inconsistencies and texture blurring. Removing the pose-tracking mechanism (\"w/o Pose tracking\") results in a PSNR of 36.14 and LPIPS of 0.049, showing moderate performance decline due to motion estimation errors in dynamic objects, though static background reconstruction remains relatively stable. Our full method (\"Ours\") achieves the highest PSNR (36.43) and SSIM (0.971) with the lowest LPIPS (0.047), demonstrating that the combination of LiDAR-SfM fusion, feature prediction, and pose tracking is essential for achieving high-fidelity dynamic scene reconstruction. Figure 9 is a visualization comparison diagram of the ablation experiments, aiming to explore the roles of the feature prediction and pose-tracking modules in our research method. The experiment sets up four groups of comparisons: \"Ours\" (the complete method), \"Without Feature prediction\" (the method with the feature prediction module removed), \"Without Pose tracking\" (the method with the pose-tracking module removed), and \"Ground Truth\" (the real-world scene). The first-row images depict an intersection scene where it is dark and the ground is wet and reflective. In the \"Ours\" image, objects are clear with rich details; in the \"Without Feature prediction\" image, it is blurry, and object outlines and details are missing; in the \"Without Pose tracking\" image, vehicles have obvious trailing. The second-row urban street scene images show similar results. The \"Without Feature prediction\" image has reduced clarity and lost texture details, and the \"Without Pose tracking\" image has blurry and ghosted vehicles. From this, it is evident that the feature prediction module is of great significance for image clarity and detail restoration, and the pose-tracking module is indispensable for the accurate representation of dynamic objects. Our complete \"Ours\" method can effectively avoid these problems and better restore the real-world scene. This not only validates the effectiveness of these two modules but also provides a solid foundation for the overall performance of our proposed method.