# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 4 Results
### Ablation Study  
We perform ablation experiments with RD-SLAM on room0 sequences of the Replica dataset to evaluate the effectiveness of the dual keyframe selection strategy and scale regularization. As shown in Figure 7, we exclude the mapping keyframes and scale regularization to verify their effects on the system. Without mapping keyframes, the resulting rendered image produces voids at the edges due to the ineffective implementation of the densification strategy. With scale regularization removed, the scale of the 3D Gaussian is not effectively constrained, resulting in the system being unable to render the fine details of the scene.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **Metrics** & **Room0** & **Room1** & **Room2** & **Office0** & **Office1** & **Office2** & **Office3** & **Office4** & **Avg.** \\ \hline \multirow{3}{*}{NICE-SLAM [29]} & PSNR \(\uparrow\) & 22.12 & 22.47 & 24.52 & 29.07 & 30.34 & 19.66 & 22.23 & 24.94 & 24.42 \\  & SSIM \(\uparrow\) & 0.689 & 0.757 & 0.814 & 0.874 & 0.886 & 0.797 & 0.801 & 0.856 & 0.809 \\  & LPIPS \(\downarrow\) & 0.330 & 0.271 & 0.208 & 0.229 & 0.181 & 0.235 & 0.209 & 0.198 & 0.233 \\ \hline \multirow{3}{*}{Point-SLAM [32]} & PSNR \(\uparrow\) & 32.40 & 34.08 & 35.50 & 38.26 & 39.16 & 33.99 & 33.48 & 33.49 & 35.17 \\  & SSIM \(\uparrow\) & 0.974 & **0.977** & 0.982 & 0.983 & 0.986 & 0.960 & 0.960 & **0.979** & **0.975** \\  & LPIPS \(\downarrow\) & 0.113 & 0.116 & 0.111 & 0.100 & 0.118 & 0.156 & 0.132 & 0.142 & 0.124 \\ \hline \multirow{3}{*}{GS-SLAM [20]} & PSNR \(\uparrow\) & 31.56 & 32.86 & 32.59 & 38.70 & 41.17 & 32.36 & 32.03 & 32.92 & 34.27 \\  & SSIM \(\uparrow\) & 0.968 & 0.973 & 0.971 & **0.986** & **0.993** & **0.978** & **0.970** & 0.968 & **0.975** \\  & LPIPS \(\downarrow\) & 0.094 & 0.075 & 0.093 & 0.050 & **0.033** & 0.094 & 0.110 & 0.112 & 0.082 \\ \hline \multirow{3}{*}{SplaTAM [19]} & PSNR \(\uparrow\) & 32.65 & 33.68 & 35.11 & 37.95 & 38.82 & 32.08 & 29.99 & 31.77 & 34.01 \\  & SSIM \(\uparrow\) & **0.977** & 0.969 & **0.984** & 0.981 & 0.981 & 0.967 & 0.950 & 0.947 & 0.970 \\  & LPIPS \(\downarrow\) & 0.070 & 0.102 & 0.075 & 0.087 & 0.097 & 0.098 & 0.119 & 0.156 & 0.101 \\ \hline \multirow{3}{*}{**Ours**} & PSNR \(\uparrow\) & **34.55** & **37.08** & **37.92** & **42.62** & **42.66** & **36.19** & **35.93** & **38.08** & **38.13** \\  & SSIM \(\uparrow\) & 0.956 & 0.969 & 0.973 & 0.984 & 0.982 & 0.971 & 0.963 & 0.970 & 0.971 \\ \cline{1-1}  & LPIPS \(\downarrow\) & **0.057** & **0.045** & **0.050** & **0.028** & 0.037 & **0.048** & **0.049** & **0.048** & **0.045** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Map quality on Replica.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **System FPS \(\uparrow\)** & **ATE RMSE [cm] \(\downarrow\)** & **PSNR [dB] \(\uparrow\)** \\ \hline Point-SLAM [32] & 0.19 & 0.61 & 32.40 \\ SplaTAM [19] & 0.17 & 0.27 & 32.65 \\
**Ours** & **10.57** & **0.15** & **34.55** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Runtime on Replica room0.  
Table 8 shows the ablation experiments data for the dual keyframe selection strategy and scale regularization. With only tracking keyframes, the rendering results of our method are drastically degraded by the failure to add 3D Gaussians representing the scene effectively. In contrast, our proposed dual keyframe selection strategy improves the PSNR by 10.81 dB by adding more accurate 3D Gaussians. Scale regularization losses can significantly improve the mapping performance with precise shape constraints compared to supervising with only color and depth losses. Adding the scale regularization loss decreases the LPIPS by 0.188. Our complete implementation yields higher quality and more detailed rendering results.  
Figure 6: Comparison of the rendering visualization results of RD-SLAM proposed in this paper with other SOTA methods on three sequences in the Replica dataset. The second, fourth, and sixth rows show zoomed-in details of the colored squares.