# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 4 Experiments
### Three-Dimensional Object Reconstruction  
To evaluate the effectiveness of TGA-GS for 3D reconstruction under low-light conditions, we conducted experiments on two scenes, Fruits1 and Cup, using 2 DGS [94] and RaDe-GS [95] methods to reconstruct 3D models from low-light RGB and thermal inputs. As illustrated in Figures 5 and 6, the leftmost column displays the input low-light RGB and thermal images for each scene. The middle columns show reconstruction results from 2 DGS and RaDe-GS, with the upper row representing RGB-only reconstructions and the lower  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metrics** & **Methods** & **Fruits1** & **Fruits2** & **Cup** & **Laptop** & **Hairdryer** & **Avg.** \\ \hline \multirow{5}{*}{PSNR (dB)\(\uparrow\)} & 3 DGS [2] & 37.26 & 36.37 & 38.43 & 41.79 & 36.77 & 38.13 \\  & ThermalGaussian [62] & 23.47 & 25.98 & 26.74 & 23.53 & 26.42 & 25.23 \\  & 2 DGS [94] & 37.55 & 36.21 & 38.11 & 39.85 & 37.18 & 37.78 \\  & RaDe-GS [95] & 38.58 & 35.75 & 38.37 & 39.70 & 36.49 & 37.78 \\  & Ours & **39.98** & **39.66** & **42.12** & **44.05** & **38.09** & **40.77** \\ \hline \multirow{5}{*}{SSIM\(\uparrow\)} & 3 DGS & **0.941** & 0.925 & 0.953 & **0.970** & 0.941 & **0.946** \\  & ThermalGaussian & 0.727 & 0.695 & 0.743 & 0.635 & 0.715 & 0.703 \\  & 2 DGS & 0.938 & 0.918 & 0.945 & 0.952 & **0.943** & 0.940 \\  & RaDe-GS & 0.929 & 0.924 & 0.948 & 0.955 & 0.936 & 0.939 \\  & Ours & 0.937 & **0.939** & **0.960** & 0.963 & 0.852 & 0.930 \\ \hline \multirow{5}{*}{LPIPS \(\downarrow\)} & 3 DGS & 0.078 & 0.115 & 0.131 & 0.055 & 0.130 & 0.102 \\  & ThermalGaussian & 0.249 & 0.264 & 0.319 & 0.240 & 0.338 & 0.282 \\  & 2 DGS & 0.080 & 0.122 & 0.147 & 0.076 & 0.134 & 0.112 \\  & RaDe-GS & 0.087 & 0.126 & 0.153 & 0.077 & 0.149 & 0.118 \\  & Ours & **0.075** & **0.075** & **0.110** & **0.028** & **0.124** & **0.082** \\ \hline \multirow{5}{*}{Mem. (MB) \(\downarrow\)} & 3 DGS & 1433 & 1685 & 1447 & 1511 & 1403 & 1496 \\  & ThermalGaussian & 945 & 1363 & 1179 & 1147 & 1075 & 1142 \\  & 2 DGS & 827 & 1089 & 951 & 1177 & 717 & 952 \\  & RaDe-GS & 865 & 1135 & 887 & 941 & 817 & 929 \\  & Ours & **551** & **571** & **502** & **596** & **480** & **540** \\ \hline \multirow{5}{*}{FPS \(\uparrow\)} & 3 DGS & 307 & 428 & 342 & 333 & 334 & 349 \\  & ThermalGaussian & **442** & 487 & 358 & 365 & 380 & 406 \\ \cline{1-1}  & 2 DGS & 215 & 239 & 220 & 152 & 225 & 210 \\ \cline{1-1}  & RaDe-GS & 205 & 283 & 225 & 228 & 228 & 232 \\ \cline{1-1}  & Ours & **439** & **544** & **505** & **447** & **445** & **476** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results of RGB view synthesis (**best**, second best).  
Figure 4: Qualitative evaluation of RGB view synthesis.  
row thermal-only reconstructions. The rightmost column presents TGA-GS's multi-modal (RGB+thermal) reconstruction results.  
The results demonstrate that TGA-GS achieves more accurate reconstructions compared to 2 DGS and RaDe-GS. In the Fruits1 scene, TGA-GS successfully distinguishes individual fruits such as bananas, while other methods struggle to resolve fine-grained structures. For the Cup scene, despite using a cup filled with hot water, thermal-only reconstructions exhibit inferior performance compared to RGB-only results. This degradation may stem from the low contrast of thermal images, which introduces ambiguity in depth estimation. In contrast, TGA-GS leverages complementary information from both modal  
Figure 5: Qualitative evaluation of mesh reconstruction on Fruits1 scene.  
Figure 6: Qualitative evaluation of mesh reconstruction on Cup scene.  
ities to generate higher-quality 3D models. The fused approach effectively mitigates the limitations of individual modalities, highlighting the advantages of multimodal integration in low-light 3D reconstruction.