## 4 Discussion  
The demonstration involving two users entering the industrial metaverse provided a real-time validation of the solution's outcomes (Section 3.1). The tangible view showcased the capabilities and possibilities offered by the industrial metaverse, emphasizing its real-time performance. Figure 16: Comparison of mesh-based model (**left**) versus NeRF-based model in Unity (**right**). Figure 17: Comparison of mesh-based model versus NeRF model in Unreal. (**a**) Mesh-based model. (**b**) NeRF model. The comparison between real industrial models (Figure 11) and their counterparts obtained in the metaverse (Figure 12) confirms a high degree of realism. This affirmation underscores that the models obtained closely resemble their real-world counterparts, contributing to a heightened sense of realism within the metaverse. As demonstrated in this study, models based on Instant NeRFs offer a more realistic visualization of objects compared to conventional techniques. This enhanced realism is particularly noticeable in objects featuring light reflections, fur, or fiber elements, as illustrated in the case studies presented in Figures 16 and 17. However, interaction with non-rigid objects such as plush toys may not provide a fully immersive experience, as manipulation does not result in deformation. Therefore, a potential avenue for further investigation is to introduce interactive capabilities where non-rigid models dynamically respond to user interaction, thereby significantly enhancing immersion. It is worth noting that the computational cost of NeRF-based models is much higher than traditional mesh-based models. Additionally, their integration into virtual environment development applications is not straightforward, requiring the use of external plugins such as LUMA to import them into Unreal-type editors. However, there are ways to work with NeRF approximations to reduce their computational cost. For example, one solution to reduce data computing consumption is by using the RT-NeRF technique [27]. As indicated in the study, NeRF's real-time performance on AR/VR devices is limited by uniform point sampling and the dense calculations required, despite its excellent image quality. RT-NeRF represents a significant advancement in real-time 3D rendering technology, particularly for Augmented and Virtual Reality. By effectively addressing NeRF's performance issues, RT-NeRF significantly improves speed without compromising visual quality. Another solution could be to convert NeRF to the NVOL format [28], which stores NeRF in a compressed and optimized manner using a data structure called an octree. An octree divides the 3D space into smaller cubes, allowing for quick access to information for each point. Thus, an NVOL can display NeRF in Unreal Engine without the need to compute neural radiance on each ray. Table 1 shows the performance of NeRF and NVOL on some current Nvidia cards, using the Stable Diffusion benchmark with FP32 precision and a batch size of 16 [29]. The data are expressed in samples per second (SPS), indicating the training and inference speed of the models. As shown in the table, the use of NVOL results in a 10\(\times\) acceleration in NeRF performance regardless of the card used. This is because NVOL reduces the computational load of neural radiance and better utilizes GPU memory and bandwidth. Another aspect to consider is the challenges associated with editing NeRFs. Unlike meshes, which offer explicit control over vertices and surfaces, NeRFs encode scenes in a continuous, volumetric manner, complicating tasks such as geometry and appearance editing [30]. A key challenge in NeRF editing lies in the absence of an object-centric decomposition, making it arduous to isolate and modify specific scene elements without comprehensive retraining or elaborate manipulation strategies [31]. Moreover, translating 2D edits into