## 5 Conclusions
#### Limitations and Future Work  
While our model has shown promising results, it is essential to recognize its limitations. In terms of computational efficiency, although our model exhibits high accuracy, the computational demand, particularly in terms of memory usage, might be challenging for real-time applications or systems with limited resources. Additionally, the model was primarily trained and tested on industrial datasets, and its performance on diverse and more generic datasets remains to be explored. Handling larger or more intricate 3D models might require further optimizations, as the current architecture might not scale linearly with increasing complexity. Moreover, the model, though trained with denoised data, might be  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Loss** & **Score** \\ \hline Point Cloud (EMD) & 0.0121665 \\ \hline RGB 2D (MSE) & 0.0986291 \\ \hline Depth 2D (MSE) & 0.0117503 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loss scores of the visual results.  
sensitive to noisy or imperfect input images, and robustness against such imperfections is crucial for real-world deployments.  
Given these limitations, future work can focus on enhancing computational efficiency through model pruning or adopting more lightweight architectures to facilitate real-time applications. Expanding the dataset scope to ensure the model's adaptability across various scenarios and object types is essential for improving its generalizability. Introducing noise augmentation during training can enhance robustness against imperfect input images, making the model more resilient to real-world conditions. Investigating multimodal input fusion techniques to leverage diverse data types (e.g., combining visual, depth, and thermal data) could result in more detailed and accurate 3D reconstructions.  
In conclusion, while our proposed IndustrialNeRF model has successfully demonstrated the feasibility of unsupervised 3D reconstruction from high-resolution industrial images, it opens avenues for further research and development. Addressing the identified limitations will enhance the practical applicability of our approach, making it a more robust and scalable solution for various industrial and real-world applications. As technology progresses, we anticipate that 3D reconstruction will become increasingly integral to numerous fields, driving continuous innovation and improvement in this domain.  
Conceptualization, Z.N. and L.Z.; methodology, Z.N.; software, J.X. and H.L.; validation, H.Z., J.X. and H.L.; formal analysis, H.Z.; investigation, H.Z.; resources, L.Z.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, Z.N. and L.Z.; visualization, J.X.; supervision, Z.N.; project administration, L.Z.; funding acquisition, H.Z. and Z.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under Grant No. 2020 YFB173200, and the National Natural Science Foundation of China under Grant number 72188101.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors have no relevant financial or non-financial interests to disclose.