## 4 Experiments
### Ablation Study  
To further validate the effectiveness of the proposed method, we conducted extensive ablation experiments using the MaiCity dataset. The following tables present the results with different experimental settings, including variations in the sliding window length and rendering loss functions.  
Table 4 shows the quantitative evaluation results with varying sliding window lengths. The sliding window strategy plays a crucial role in improving the performance of the system. The highest values for each metric are highlighted in bold. The results indicate that omitting the sliding window strategy (No Window) leads to a significant performance drop across all metrics. The system achieves the best performance with a window length of \(w=20\), as evidenced by the lowest completion, accuracy, and Chamfer-L1 distance coupled with the highest F-score. However, the performance starts to degrade as \(w\) exceeds this optimal value, especially in terms of completion and accuracy.  
The degradation in performance for larger window lengths, particularly \(w=40\), can be attributed to increased computational complexity, reduced sensitivity to local scene  
Figure 8: Our method effectively removes dynamic objects such as pedestrians, vehicles from the LiDAR point cloud, achieving cleaner static scene reconstruction; in contrast, NeRF-LOAM fails to eliminate dynamic interference, resulting in artifacts and errors.  
variations, and accumulated dynamic interference. Larger window sizes require more computational resources, which may lead to delays and reduced real-time performance. Additionally, a larger window may fail to capture rapid changes in local scenes, resulting in less effective dynamic object removal. Furthermore, dynamic objects may persist across multiple frames in larger windows, leading to artifacts in the static scene reconstruction. These factors collectively explain why \(w=40\) exhibits the worst performance among the tested window lengths.  
From these results, it is clear that the window length plays a critical role in fine-tuning the system's performance. As \(w\) becomes larger than 20, our proposed method becomes less sensitive to local scene variations, resulting in poorer performance.  
Table 5 presents the ablation study results with different rendering losses. We evaluated the impact of each individual loss function--completion loss \(\mathcal{L}_{c}\), depth loss \(\mathcal{L}_{d}\), and edge loss \(\mathcal{L}_{edge}\)--on overall system performance. As seen in the results, omitting any one of these losses reduces the overall accuracy and completeness of the reconstruction.  
For example, when the completion loss \(\mathcal{L}_{c}\) is excluded, the system experiences a slight degradation in performance, with higher completion and Chamfer-L1 distance values. Exclusion of the depth loss \(\mathcal{L}_{d}\) also causes a noticeable drop in accuracy, indicating its importance for depth-aware optimization. Finally, removing the edge loss \(\mathcal{L}_{edge}\) leads to the largest performance drop in terms of both completion and F-score, as this loss function significantly contributes to preserving structural details in the reconstruction.  
These findings strongly suggest that all three loss functions play an essential role in optimizing different aspects of the system, including geometric fidelity, accuracy, and feature preservation.