AN OPEN-SOURCE APPROACH FOR MULTI-SENSOR SCAN-TO-BIM WORKFLOWS

Open-source methodologies promote broader access to knowledge, tools, and technological development by supporting reproducible and community-driven advancements. By integrating digital surveying into these workflows, it is possible to reduce the existing dependency of the construction field on proprietary software solutions. For this purpose, this study aims to develop an open-source Scan-to-BIM approach, based on multi-sensor surveying. Scan-to-BIM refers to the process of using captured spatial data to create accurate as-built/as-is BIM models, facilitating tasks such as renovation and maintenance throughout the infrastructure lifecycle. This study employs the open-source software Blender, paired with the Bonsai add-on, as the central platform for compiling data gathered from aerial and handheld surveys conducted at FEUP. The case study integrates three major sensing technologies to capture both exterior and interior data: UAV-based photogrammetry and thermography (which enabled an additional Image-to-BIM process), as well as LiDAR scanning via an iOS device. The results prove that an affordable open-source Scan-to-BIM workflow is fully achievable, thus enabling smaller entities to access high-quality 3D documentation. Photogrammetry achieved millimetric-level accuracy relative to the real building, while the LiDAR surveys required little to no scale adjustment to match the results of both sensors, indicating the successful integration of multiple data sources within the open-source workflow. Finally, Blender, along with the Bonsai add-on, proved to be a versatile platform capable of efficiently managing complex Scan-to-BIM processes. Overall, the study demonstrates that open-source tools can support advanced BIM tasks; therefore, offering a practical and cost-effective alternative to proprietary software solutions.