Work

Indoor 3D point cloud reconstruction for scan-to-BIM automation

Year: 2026

Type: article

Abstract: In today's urbanizing world, the demand for intelligent digital representations of indoor spaces is increasing, driving advancements in automated 3D modeling and data integration. Building Information Modeling (BIM) transforms spatial digitization by converting raw point clouds into actionable models, supporting applications such as building management and smart city planning. Existing reviews on scan-to-BIM often focus on specific stages or technologies, overlooking a comprehensive workflow perspective. This paper employs an integrated review approach, combining scientometric analysis with a systematic qualitative review of the entire scan-to-BIM framework. It examines methods for reconstructing and modeling both structured and unstructured indoor elements using traditional and recent deep learning approaches. The review identifies key methodologies, limitations, and automation challenges, data quality, and modeling complexity, and outlines future directions to enhance scan-to-BIM workflows. By providing a comprehensive overview, this study aims to advance the understanding of scan-to-BIM automation and its contribution to indoor 3D BIM modeling.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2026.106853

Authors: Mostafa Mahmoud, Yaxin Li, Mahmoud Adham, Ahmed Mansour, Wu Chen

Institutions: Hong Kong Polytechnic University, Cairo University

Language: English

Topic: 3D Surveying and Cultural Heritage

Subfield: Geology

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $5,490