Automated material-aware BIM generation using deep learning for comprehensive indoor element reconstruction
Year: 2025
Type: article
Abstract: Automating 3D reconstruction of indoor environments is essential for scene understanding in Building Information Modeling (BIM). This paper addresses the challenge of integrating geometric and material attributes in scan-to-BIM processes. A deep learning-based framework is developed to automatically extract and integrate geometric and material attributes from point clouds, incorporating an enhanced instance segmentation network, a material classification model, and an automated BIM integration workflow for accurate indoor modeling. The proposed framework reconstructs accurate 3D BIM models of space-forming and space-occupying elements while preserving key attributes. Experimental results show significant improvements in instance segmentation accuracy, with reconstructed 3D BIM models achieving over 98 % correctness and completeness , while the material classification model attains a point-based weighted F 1 − score of 0.973 and an object-based accuracy of 94.70 %. These findings advance automated BIM generation, enhancing building planning , asset management, and sustainable design while inspiring further developments in scan-to-BIM automation.
Source: Automation in Construction
Authors: Mostafa Mahmoud, Yaxin LI, Mahmoud Adham, Wu CHEN
Institutions: Hong Kong Polytechnic University, Cairo University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,490
OpenAlex ID: W4409433065