## 4 Results and Evaluation
### Subjective Evaluation  
This aspect of our model evaluation was carried out by looking directly into the.ply and.obj output files and manually checking for excess points, noise, and imperfections by 3D modeling experts (Figure 6). We established the Luma Labs Capture web app's implementation of GS and their mesh extraction algorithm [51] as a baseline for quality control by comparison with our locally implemented GS and Mip-splatting-SuGaR compos \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**No Mesh Extraction Method (Except SuGaR)**} \\ \hline \multicolumn{4}{c}{Romanesque–Mudéjar dataset} \\ \hline
**Method/metric** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **LPIPS \(\downarrow\)** \\ \hline SuGaR Romanesque– & & & \\ Mudejar/15 K & 9.88 \(\uparrow\) & 0.483 \(\uparrow\) & 0.630 \(\uparrow\) \\ \hline \multicolumn{4}{c}{Mip-NeRF360 dataset} \\ \hline Plenoxels & 22.02 & 0.542 & 0.465 \\ INGP-Base & 23.47 & 0.571 & 0.416 \\ INGP-Big & 23.57 & 0.602 & 0.375 \\ Mip-NeRF360 & 25.79 & 0.746 & 0.247 \\ \hline
3 DGS & 26.40 & 0.805 & 0.173 \\ SuGaR [25]/15 K & 24.40 & 0.699 & 0.301 \\ \hline \multicolumn{4}{c}{**With the mesh extraction method**} \\ \hline \multicolumn{4}{c}{Romanesque–Mudéjar dataset} \\ \hline
**Method/metric** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **LPIPS \(\downarrow\)** \\ \hline SuGaR Romanesque– & 9.88 \(\uparrow\) & 0.483 \(\uparrow\) & 0.630 \(\uparrow\) \\ \hline \multicolumn{4}{c}{Mip NeRF360 dataset} \\ \hline Mobile NeRF & 21.95 & 0.470 & 0.470 \\ \hline NeRFMeshing & 22.23 & - & - \\ \hline BakedSDF & 22.47 & 0.585 & 0.349 \\ \hline SuGaR [25]/2 K & 22.97 & 0.648 & 0.360 \\ \hline SuGaR [25]/7 K & 24.16 & 0.691 & 0.313 \\ \hline SuGaR [25]/15 K & 24.40 & 0.699 & 0.301 \\ \hline \hline \end{tabular} The explanation of the arrows direction (\(\uparrow\), \(\downarrow\)) is the same as in Table 1. Data compiled by the authors, based on [25].  
\end{table} 
Table 3: Objective evaluation metrics comparing GS, Mip-splatting and SuGaR only on outdoor scenes using our own Romanesque–Mudéjar datasets. Averages are shown to evaluate overall performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method/Metric** & **SSIM \(\uparrow\)** & **PSNR \(\uparrow\)** & **LPIPS \(\downarrow\)** & **Train** \\ \hline GS/30 K & 0.705 & 22.610 & 0.371 & 0 h 21 min 31 s \\ \hline Mip-splatting/30 K & 0.665 & 20.660 & 0.335 & 0 h 22 min 00 s \\ \hline SuGaR/15 K & 0.482 & 9.876 & 0.630 & 2 h 01 min 15 s \\ \hline Average (all methods used) & 0.618 & 17.716 & 0.445 & 2 h 22 min 23 s \\ \hline \hline \end{tabular} The explanation of the arrows direction (\(\uparrow\), \(\downarrow\)) is the same as in Table 1. Data compiled by the authors.  
\end{table} 
Table 2: Objective metrics for evaluating and comparing the SuGaR Romanesque–Mudéjar performances with other Mip-NeRF360 dataset SOTA performances only on outdoor scenes in [25]. Results marked with (\(\uparrow\)) were obtained in our experiments; all others have been directly transcribed from their original papers. ite system instances. Inspecting both Luma Labs GS capture web app's results and our own using Mip-splatting and SuGaR in experiment 2, we can see that holes are observed around roughly the same rooftop area in both cases (Figure 7). This points towards a shortcoming in our dataset, meaning that it only comprises images at a human observer's height; thus, there are no images shot from high altitude, such as bird's-eye views. This, in turn, leads us to believe that vision information is getting lost, such as points around the rooftop. Furthermore, in agreement with our SuGaR step's PSNR being significantly lower, we can see that the \"real\" 3D points and Gaussians get mixed in with the noise (i.e., outlier  
Figure 6: Rooftop with prediction errors. A possible solution would be to increase the dataset to include a bird’s-eye view, so the model can observe the rooftop and predict its point positions [X, Y, Z].