PSI - Issue 78

Simone Felicioni et al. / Procedia Structural Integrity 78 (2026) 1285–1292

1292

Fig. 7: (a) Interactive marker and (b) a corresponding view associated to a POI.

5. Conclusions and Future Directions This paper presents an immersive inspection framework that combines LiDAR-based 3D mapping and aerial image acquisition to support structural damage monitoring through VR. The system enables users to explore a digital replica of the structure in an immersive setting and monitor the temporal evolution of damages. Future developments will focus on increasing the autonomy and scalability of the system. In particular, the integration of deep learning-based crack detection will eliminate the need for manual video review, enabling real-time identification of structural anomalies. In addition, to enhance the realism and informativeness of the virtual inspection, future versions of the framework will explore the use of neural rendering techniques, e.g., Neural Radiance Fields (NeRF) or Gaussian Splatting, to generate dense, photorealistic representations of the structure. Acknowledgements The project is supported by the University of Perugia, Fondo di Ricerca di Ateneo 2022, Project “MiRA: Mixed Reality and AI Methodologies for Immersive Robotics". References Feng, Z., González, V., Mutch, C., Amor, R., Rahouti, A., Baghouz, A., Li, N., Cabrera-Guerrero G., 2020. Towards a customizable immersive virtual reality serious game for earthquake emergency training. Advanced Engineering Informatics 46, pp. 101134. Sadhu, A., Peplinski, J., Mohammadkhorasani, A., Moreu, F., 2023. A Review of Data Management and Visualization Techniques for Structural Health Monitoring Using BIM and Virtual or Augmented Reality. Journal of Structural Engineering 149. Lorusso, P., De Iuliis, M., Marasco, S., Domaneschi, M., Cimellaro, GP., Villa, V., 2022. Fire Emergency Evacuation from a School Building Using an Evolutionary Virtual Reality Platform. Buildings 12(2):223. Pantoja-Rosero, B.G., Achanta, R., Beyer, K., 2023. Damage-augmented digital twins towards the automated inspection of buildings. Automation in Costruction 150. Martins, A.C.P., Castellano, I.R., Lenz César Júnior, K.M., Franco de Carvalho, J.M., Bellon, F. G., de Oliveira, D.S., Ribeiro, J.C.L., 2024. BIM based mixed reality application for bridge inspection. Automation in Contruction 168. Hausler, S., Garg, S., Xu, M., Milford, M., Fischer, T., 2021. Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.p. 14141-14152. Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., and Sivic, J., 2016. NetVLAD: CNN Architecture for Weakly Supervised Place Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.p. 5297-5307.