用全景图重建消防设备3D模型，提升建筑信息管理精度
Semantic BIM enrichment for firefighting assets: Fire-ART dataset and panoramic image-based 3D reconstruction
- 基于全景图像与改进球面投影，实现消防设备精准定位
- 在两个真实场景中达73%-88%的识别准确率，定位误差小于0.63米
- 开源数据集含2626张图、6627个实例，适合消防数字化研究
消防装备的库存管理对应急准备、风险评估和现场灭火至关重要。传统方法因自动化识别与重建能力有限而效率低下。为此，本研究提出Fire-ART数据集，并开发一种基于全景图像的三维重建方法，实现消防设备在建筑信息模型（BIM）中的语义增强。Fire-ART数据集涵盖15类基础设备，包含2,626张图像和6,627个实例，是当前规模较大且公开可用的装备识别数据集。重建方法融合改进的立方体映射转换与基于半径的球面相机投影，显著提升识别与定位精度。通过两个实际案例验证，该方法在不同场景下分别达到73%和88%的F1分数，定位误差分别为0.620米和0.428米。Fire-ART数据集与重建方案为消防设备的精准数字化管理提供了重要资源与可靠技术支撑。
原文摘要 · Abstract (English)
Inventory management of firefighting assets is crucial for emergency preparedness, risk assessment, and on-site fire response. However, conventional methods are inefficient due to limited capabilities in automated asset recognition and reconstruction. To address the challenge, this research introduces the Fire-ART dataset and develops a panoramic image-based reconstruction approach for semantic enrichment of firefighting assets into BIM models. The Fire-ART dataset covers 15 fundamental assets, comprising 2,626 images and 6,627 instances, making it an extensive and publicly accessible dataset for asset recognition. In addition, the reconstruction approach integrates modified cube-map conversion and radius-based spherical camera projection to enhance recognition and localization accuracy. Through validations with two real-world case studies, the proposed approach achieves F1-scores of 73% and 88% and localization errors of 0.620 and 0.428 meters, respectively. The Fire-ART dataset and the reconstruction approach offer valuable resources and robust technical solutions to enhance the accurate digital management of fire safety equipment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。