Mansour Mehranfar

Mansour Mehranfar is a Researcher at the Chair of Computing in Civil and Building Engineering, Technical University of Munich (TUM). He contributes to advancing AI applications in digital twinning of the built environment through the AI4Twinning research initiative, focusing on automated generation of semantic building models from point cloud data and imagery.

His research centers on Digital Twinning, Building Information Modeling (BIM), and Computer Vision, with specific expertise in point cloud processing, semantic segmentation, and 3D reconstruction. He develops AI-driven frameworks that integrate deep learning with geometric modeling to convert raw sensor data into semantically enriched digital representations of buildings and infrastructure components.

Recent publications reveal a strong emphasis on staircase modeling, indoor space documentation, and domain adaptation techniques. Key innovations include hybrid top-down/bottom-up approaches for Manhattan-world structures, parametric prototype model fitting, and multi-task learning frameworks that simultaneously handle scene parsing and 3D reconstruction from single images.

Dr. Mehranfar actively supervises Master's theses, guiding students in topics such as load-bearing wall detection, staircase modeling automation, and BIM change management. He teaches Engineering Databases at TUM and collaborates within the university's BIM-Lab ecosystem, leveraging facilities for robotic fabrication and mobile machinery research.