Dr. Thomas Czerniawski is an assistant professor in the School of Sustainable Engineering and the Built Environment. His expertise is in construction management, computer vision, technology adoption, and building information modeling. He has created computer vision systems for pipe-spool fabrication quality control, building information model updating, and construction progress monitoring. He has worked on several heavy civil construction projects in transportation and power generation.
PhD, The University of Texas at Austin
MASc, The University of Waterloo
BASc, The University of Waterloo
Remote sensing, computer vision, deep learning, digital twin
Czerniawski, T., & Leite, F. (2020). Automated segmentation of RGB-D images into a comprehensive set of building components using deep learning. Advanced Engineering Informatics, 45, 101131, https://doi.org/10.1016/j.aei.2020.101131
Czerniawski, T., Leite, F. (2020) Automated digital modeling of existing buildings: A review of visual object recognition methods. Automation in Construction, 113, 103131, ISSN 0926-5805, https://doi.org/10.1016/j.autcon.2020.103131.
Czerniawski, T., Sankaran, B., Nahangi, M., Haas, C., Leite, F. (2018) 6D DBSCAN-based segmentation of building point clouds for planar object classification. Automation in Construction, Volume 88, April 2018, Pages 44-58, ISSN 0926-5805, https://doi.org/10.1016/j.autcon.2017.12.029.
Czerniawski T., Leite F. (2018). “3DFacilities: Annotated 3D Reconstructions of Building Facilities”. In: Smith I., Domer B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science, vol 10863. Springer, Cham, https://doi.org/10.1007/978-3-319-91635-4_10
Czerniawski, T., Nahangi, M., Haas, C., & Walbridge, S. (2016). Pipe spool recognition in cluttered point clouds using a curvature-based shape descriptor. Automation in Construction, Volume 71, Part 2, 2016, Pages 346-358, ISSN 0926-5805, https://doi.org/10.1016/j.autcon.2016.08.011.
Ma, J. W., Czerniawski, T., Leite, F. (2020) Semantic Segmentation of Point Clouds of Building Interiors with Deep Learning: Augmenting Training Datasets with Synthetic BIM-based Point Clouds, Automation in Construction, 113, 103144, https://doi.org/10.1016/j.autcon.2020.103144.
Nahangi, M., Czerniawski, T., Haas, C., & Walbridge, S. (2019). Pipe radius estimation using Kinect range cameras. Automation in Construction, Volume 99, March 2019, Pages 197-205, https://doi.org/10.1016/j.autcon.2018.12.015
Nahangi, M., Czerniawski, T., Haas, C., Walbridge, S., & West, J. (2015). Parallel systems and structural frames realignment planning and actuation strategy. Journal of Computing in Civil Engineering, 04015067. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000545