# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 4 Experiments
### RGB View Synthesis  
To assess the performance of GS methods for novel RGB view synthesis under low-light conditions, we performed rigorous quantitative assessments and qualitative visual comparisons. As summarized in Table 2, our approach surpasses existing methods in critical visual metrics, including PSNR and LPIPS, while maintaining substantially lower computational resource requirements and high computational efficiency. The quantitative analysis reveals that our method achieves state-of-the-art performance with a memory footprint of 540 MB, delivering a PSNR of 40.77 dB and LPIPS of 0.082. In contrast, the second-best performer (3 DGS [2]) demonstrates comparable but inferior visual metrics (2.64 dB lower PSNR and 0.02 higher LPIPS) while consuming 1496 MB of memory (approximately three times the model's requirements) and 349 FPS inference speed (approximately 30% slower than ours).  
Figure 4 provides qualitative comparisons across three representative scenarios: Fruits2 (top row), Hairdryer (middle row), and Laptop (bottom row). These visualizations underscore our method's enhanced fidelity in preserving fine details under illumination-starved conditions. Notably, in the Laptop scene, our approach maintains crisp structural details such as labels, whereas competing methods exhibit blurred textures and loss of high-frequency features. This performance gap aligns with the quantitative metrics, confirming our framework's superiority in balancing reconstruction accuracy with computational efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metrics** & **Methods** & **Fruits1** & **Fruits2** & **Cup** & **Laptop** & **Hairdryer Avg.** \\ \hline \multirow{4}{*}{PSNR (dB)\(\uparrow\)} & 3 DGS [2] & **37.34** & 29.29 & 29.47 & 35.55 & **30.90** & 32.51 \\  & ThermalGaussian [62] & 18.82 & 18.65 & 17.01 & 10.31 & 17.64 & 16.49 \\  & 2 DGS [94] & 28.52 & 22.79 & 27.77 & 36.12 & 28.48 & 28.74 \\  & RaDe-GS [95] & 32.56 & 28.46 & **30.24** & 37.25 & 27.75 & 31.25 \\  & Ours & 35.51 & **30.25** & 29.72 & **38.94** & 29.50 & **32.78** \\ \hline \multirow{4}{*}{SSIM\(\uparrow\)} & 3 DGS & **0.967** & **0.935** & 0.948 & **0.975** & **0.951** & **0.955** \\  & ThermalGaussian & 0.743 & 0.781 & 0.782 & 0.554 & 0.744 & 0.721 \\  & 2 DGS & 0.922 & 0.872 & 0.952 & 0.972 & 0.948 & 0.933 \\  & RaDe-GS & 0.952 & 0.932 & **0.954** & 0.974 & 0.943 & 0.951 \\  & Ours & 0.952 & 0.933 & 0.949 & 0.972 & 0.899 & 0.941 \\ \hline \multirow{4}{*}{LPIPS \(\downarrow\)} & 3 DGS & **0.108** & 0.156 & 0.099 & 0.058 & **0.089** & 0.102 \\  & ThermalGaussian & 0.500 & 0.361 & 0.284 & 0.628 & 0.299 & 0.414 \\  & 2 DGS & 0.233 & 0.229 & 0.104 & 0.076 & 0.100 & 0.148 \\  & RaDe-GS & 0.162 & 0.168 & 0.095 & 0.065 & 0.108 & 0.120 \\  & Ours & 0.145 & **0.095** & **0.087** & **0.021** & 0.111 & **0.092** \\ \hline \multirow{4}{*}{Mem. (MB) \(\downarrow\)} & 3 DGS & 1467 & 1733 & 1743 & 1783 & 1563 & 1658 \\  & ThermalGaussian & 945 & 1363 & 1179 & 1147 & 1075 & 1142 \\  & 2 DGS & 659 & 1069 & 711 & 711 & 729 & 776 \\  & RaDe-GS & 939 & 1309 & 1027 & 1859 & 1049 & 1237 \\  & Ours & **551** & **571** & **502** & **596** & **480** & **540** \\ \hline \multirow{4}{*}{FPS \(\uparrow\)} & 3 DGS & 292 & 351 & 230 & 234 & 218 & 265 \\  & ThermalGaussian & **390** & 432 & **312** & **347** & 314 & **359** \\ \cline{1-1}  & 2 DGS & 260 & 251 & 212 & 173 & 223 & 224 \\ \cline{1-1}  & RaDe-GS & 201 & 298 & 209 & 140 & 184 & 206 \\ \cline{1-1}  & Ours & 387 & **441** & 301 & 315 & **318** & 352 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results of thermal view synthesis (**best**, second best).