Automated Scan-to-BIM: A deep learning-based framework for indoor environments with complex furniture elements
Year: 2025
Type: article
Abstract: Extensive 3D parametric datasets, such as Building Information Modeling (BIM) models, are crucial for reducing project costs, supporting planning, and enhancing operational efficiency in building management. However, conventional Scan-to-BIM methods rely heavily on manual or semi-automatic techniques, focusing on space-forming elements such as walls while often neglecting indoor space-occupying furniture. These methods struggle with incomplete point clouds, capturing shapes and orientations, and clustering inaccuracies. This paper presents an innovative and efficient deep learning-based framework to automatically reconstruct 3D models from point clouds. The framework accommodates diverse space-forming layouts and automatically generates parametric 3D BIM models for complex space-occupying elements like tables and chairs within the Revit platform. It also produces non-parametric 3D semantic representations of complete indoor scenes. Evaluation of publicly available and locally acquired datasets shows that the framework achieves over 98 % precision, recall, and F1-score, confirming its accuracy and effectiveness in generating complete 3D models. The reconstructed models preserve key real-world characteristics, including geometric fidelity, numerical attributes, spatial positioning, and various shapes and orientations of furniture. Seamless integration of deep learning and model-driven techniques overcomes the limitations of traditional Scan-to-BIM methods, providing an accurate and efficient solution for complex indoor space reconstruction. • An innovative deep learning-based framework for automated Scan-to-BIM is proposed. • Space-forming elements in diverse building layouts are accurately modeled. • Furniture is efficiently reconstructed while preserving real-scene characteristics. • Parametric 3D models for furniture are generated using an algorithm in Revit. • Non-parametric 3D semantic representations of different indoor elements are provided.
Source: Journal of Building Engineering
Authors: Mostafa Mahmoud, Zhebin Zhao, Wu Chen, Mahmoud Adham, Yaxin Li
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: $3,940
OpenAlex ID: W4409222262