A Framework of Panoramic Image-based 3D Semantic Reconstruction for BIM Enrichment of Firefighting Assets

Abstract

Constructing digital models of firefighting assets is essential for informed decision-making in emergency response and management. However, current practices struggle to efficiently recognize and update these assets in building information models (BIM). This study proposes a framework integrating photogrammetric reconstruction and instance segmentation to enrich BIM. The framework involves creating an expanded firefighting asset dataset and leveraging panoramic images with supervised learning for scene reconstruction and asset segmentation. Real-world evaluation illustrates it performs satisfactorily in both asset recognition and positioning. The framework offers a practical solution for modeling digital twins to support various fire emergency applications.