## 4 Results and Evaluation
### Objective Evaluation  
Our GS, Romanesque-Mudejar, in experiment 1, obtained the following scores in SSIM = 0.705, PSNR = 22.61, and LPIPS = 0.371 using GS's original implementation with no anti-aliasing. While these prices are lower than the ones obtained in the GS original paper, they are still relatively close to the mark, even outdoing Plenoxels [39] and equaling INGP-Base [34] on LPIPS (Table 1). Granted, these results are from different datasets, but LPIPS is still the most important metric concerning image analysis. In experiment 2, running the same dataset on the Mip-splatting implementation issued the following scores: SSIM = 0.665, PSNR = 20.66, and LPIPS = 0.335. These are slightly lower than the ones from the original GS model (Table 1), but it also performed better on LPIPS than both Plenoxels and INGP-Base. Furthermore,.obj mesh file verification confirmed that it is the better choice. For the SuGaR step, metrics yielded these values: SSIM = 0.483, PSNR = 9.879, and LPIPS = 0.630. These scores can be interpreted as a step in the right direction in terms of image rasterization evaluation, but are still insufficient for mesh objects, unlike other, more suitable metrics such as point accuracy % (PA) and point precision % (PP), which are intended to be used in future experiments. Figure 5: SuGaR output mesh viewed in Blender 4.0 of church 01 after “cleanup” in experiment 2. This image illustrates the type of mesh geometry that SuGaR’s outputs when using Mip-splatting’s.ply output as input. This.obj output file exhibits meshes (and therefore a point cloud) with significant noise, and they had to be “cleaned” by hand for the 3D model to be observed. Refinement set to 15,000 iterations and polygon count to one million with one Gaussian per triangle. Figure created by the authors. In Table 2, we show a comparative evaluation between the SuGaR method's application to our Romanesque-Mudejar dataset and other NeRF-based RFR and mesh extraction or reconstruction methods, specifically on outdoor scenes. These are Mobile-NeRF [49]; NeRFMeshing [37], and BakedSDF [50], as ref. [25] did in their original paper. As we can see, when used for RFR on the original Mip-NeRF dataset, SuGaR comes in second only to [23] GS on all metrics. When compared with other popular mesh extraction methods, as seen lower in Table 3, it comes in at the top on all metrics. However, when applied to our Romanesque-Mudejar dataset, it scores last on PSNR and LPIPS but surpasses Mobile-NeRF on SSIM, which highlights that it is a viable method for outdoor 3D reconstruction. However, its PSNR score highlights that the valid signal and noise are hard to distinguish and points towards the still lingering excess points in the Mip-splatting output. Training times and memory usage were not evaluated in mesh extraction metrics. Taking averages into account, our use of Mip-splatting and SuGaR together overtakes Mobile-NeRF on both SSIM and LPIPS while remaining significantly lower on PSNR. Because of this, it seems to be a viable solution for our \"photo-to-mesh\" pipeline and a significant step towards a solution to the Scan-to-HBIM problem. We decided to keep Mip-splatting even though it scores lower than GS because it reduces noise significantly, and this contributes to a more efficient point cloud-to-mesh process.