## 3 Results
### Case Study: A Comparative Analysis of Models Based on NeRF and Models Based on Photogrammetry  
In this section, 3D models obtained using standard photogrammetry techniques are compared with NeRF-based 3D models. The mesh-based models using photogrammetry were generated using RealityCapture [24] software (version 1.3), while the NeRF-based models were created using Nerfstudio [25] software (version v1.0.1). In both cases, 80 images of the objects in various positions and orientations were used as input, captured from a smartphone camera video.  
Given the challenge of using a fair quantitative metric to express the similarity between the obtained 3D models and real-world systems in the case under study, where similarity involves not only morphology but also the realism of the object including reflections, transparency, textures, and colors, the comparison will be qualitative. Models will be described and compared through images from different viewpoints as necessary.  
Firstly, Figure 14 presents the obtained models of the vision system depicted in Figure 12d. Figure 14a,b show two different viewpoints of the mesh-based photogrammetry model.  
Figure 13: Demonstration test with users in the metaverse: (**a**) navigation mode; (**b**) industrial model; (**c**) options panel; (**d**) users’ interaction; and (**e**) user interaction with different 6D models. Link to the video: users’ experience video [[https://media.upv.es/player/?id=186f82f0-613b-11](https://media.upv.es/player/?id=186f82f0-613b-11) ee-8548-1 bcf9a45 ee62]([https://media.upv.es/player/?id=186f82f0-613b-11](https://media.upv.es/player/?id=186f82f0-613b-11) ee-8548-1 bcf9a45 ee62) (accessed on 19 February 2024).  
model obtained with RealityCapture. Figure 14c,d display two very similar viewpoints to their respective ones in Figure 14a,b of the NeRF-based model obtained with Nerfstudio. It is noticeable, first and foremost, that the NeRF model better preserves the morphology of the real target system. This is evident, for instance, in the two metallic gray support rods for the camera system at the top or in the semicircular dome, where even the mesh-based system has failed and generated holes in areas with light reflection. Additionally, regarding the visual realism of the model, it can be observed how the NeRF model resembles the real target system more closely than the mesh-based model. This is particularly clear in the dome area, where the reflection of light varies depending on the viewing angle.  
While the morphology of the mesh-based model could be enhanced by introducing a greater number of input images captured with higher precision, the level of realism achieved would still be inferior to that of NeRF models.  
To further highlight the advantages of NeRF-based modeling over conventional mesh-based techniques in industrial applications, Figure 15a showcases an electric automatic polisher commonly employed by industrial robots for surface treatment tasks, particularly in sectors such as automotive manufacturing. In the image, the tool features a non-rigid and fibrous component (depicted in white) that comes into contact with the surfaces being polished. Similar to the previous case study, the modeling process relies on 80 images captured from various angles to generate the models.  
Figure 14: Comparison between photogrammetry mesh-model and NeRF model. **(a)** Photogrammetry (view 1). **(b)** Photogrammetry (view 2). **(c)** NeRF (view 1). **(d)** NeRF (view 2).  
Figure 15c depicts the NeRF-based model obtained using Nerfstudio, illustrating detailed representations of both the rigid and non-rigid components with remarkable realism, closely resembling the actual object. In contrast, Figure 15b presents the mesh-based model derived from photogrammetry using RealityCapture. Here, noticeable mesh connections are observed in the non-rigid segment, resulting in a model that deviates from the real object compared to the NeRF-based counterpart.