# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 4 Experiments
### Ablation Study on Segmentation Backbone Errors  
To investigate the sensitivity of the system to errors in the pre-trained segmentation backbone, we conducted ablation experiments on noisy segmentations. We introduced different levels of noise to the segmentation results of the pre-trained backbone and evaluated the system's performance using the mean Intersection over Union (mIoU) metric.  
We used Gaussian noise and salt-and-pepper noise to simulate errors in the segmentation results. Specifically, we controlled the intensity of noise by adjusting the variance of Gaussian noise and the proportion of salt-and-pepper noise. The experimental results are shown in Table 5.  
The results show that, as the noise level increases, the mIoU of the system decreases, indicating that the system is sensitive to errors in the pre-trained segmentation backbone. The performance degradation is more significant when the noise level reaches 0.3. Among the two types of noise, salt-and-pepper noise has a relatively greater impact on the system performance. These findings suggest that improving the accuracy of the pre-trained segmentation backbone or adding noise-resistant mechanisms can enhance the robustness of the system.  
Moreover, we evaluated the impact of different loss weight combinations on the model performance. Using the grid search method, we tested multiple weight combinations and recorded the corresponding mean Intersection over Union (mIoU) and Absolute Trajectory Error (ATE) metrics. Based on the common experience in designing semantic SLAM weights, we mainly tested three typical weight combinations: balanced, semantics-prioritized, and depth-prioritized. According to the comprehensive performance on the Replica and ScanNet datasets, we selected the depth-prioritized weight combination. Experimental results in the Table 6 show that weight combinations with different characteristics have their own advantages in specific scenarios, and the selected weights can effectively balance different loss components and improve the overall performance of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Replica**} & \multicolumn{2}{c}{**ScanNet**} \\ \cline{2-5}  & **ATE \(\downarrow\)** & **mIOU \(\uparrow\)** & **ATE \(\downarrow\)** & **mIOU \(\uparrow\)** \\ \hline \(l_{rgb}+l_{depth}\) & 20.59 & 60.51 & 22.54 & 63.24 \\ \hline \(l_{rgb}+l_{sem}+l_{depth}\) & 17.52 & 67.48 & 14.35 & 70.45 \\ \hline \(l_{rgb}+l_{sem}+l_{feat}+l_{depth}\) & **9.47** & **78.25** & **8.53** & **80.56** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study on loss function (the \(\downarrow\) means the smaller, the better, and the \(\uparrow\) means the larger, the better).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Noise Type** & **Noise Level** & **Replica mIoU** & **ScanNet mIoU** \\ \hline No Noise & 0 & 78.25 & 80.56 \\ Gaussian & 0.1 & 75.32 & 77.43 \\ Gaussian & 0.3 & 68.74 & 70.21 \\ Salt-and-Pepper & 0.1 & 74.18 & 76.39 \\ Salt-and-Pepper & 0.3 & 66.57 & 68.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study results on noisy segmentations.