| Semantic segmentation of point clouds of building interiors with deep learning: Augmenting training datasets with synthetic BIM-based point clouds JW Ma, T Czerniawski, F Leite Automation in construction 113, 103144, 2020 | 154 | 2020 |
| 6D DBSCAN-based segmentation of building point clouds for planar object classification T Czerniawski, B Sankaran, M Nahangi, C Haas, F Leite Automation in Construction 88, 44-58, 2018 | 117 | 2018 |
| Automated digital modeling of existing buildings: A review of visual object recognition methods T Czerniawski, F Leite Automation in Construction 113, 103131, 2020 | 111 | 2020 |
| Pipe spool recognition in cluttered point clouds using a curvature-based shape descriptor T Czerniawski, M Nahangi, C Haas, S Walbridge Automation in Construction 71, 346-358, 2016 | 104 | 2016 |
| Automated segmentation of RGB-D images into a comprehensive set of building components using deep learning T Czerniawski, F Leite Advanced Engineering Informatics 45, 101131, 2020 | 59 | 2020 |
| Automated building change detection with amodal completion of point clouds T Czerniawski, JW Ma, F Leite Automation in construction 124, 103568, 2021 | 36 | 2021 |
| Automated removal of planar clutter from 3D point clouds for improving industrial object recognition T Czerniawski, M Nahangi, S Walbridge, C Haas ISARC. Proceedings of the International Symposium on Automation and Robotics …, 2016 | 36 | 2016 |
| 3DFacilities: annotated 3D reconstructions of building facilities T Czerniawski, F Leite Advanced Computing Strategies for Engineering: 25th EG-ICE International …, 2018 | 31 | 2018 |
| Pipe radius estimation using Kinect range cameras M Nahangi, T Czerniawski, CT Haas, S Walbridge Automation in construction 99, 197-205, 2019 | 23 | 2019 |
| Parallel systems and structural frames realignment planning and actuation strategy M Nahangi, T Czerniawski, CT Haas, S Walbridge, J West Journal of Computing in Civil Engineering 30 (4), 04015067, 2016 | 21 | 2016 |
| An application of metadata-based image retrieval system for facility management JW Ma, T Czerniawski, F Leite Advanced Engineering Informatics 50, 101417, 2021 | 18 | 2021 |
| Semantic segmentation of building point clouds using deep learning: a method for creating training data using BIM to point cloud label transfer T Czerniawski, F Leite ASCE International Conference on Computing in Civil Engineering 2019, 410-416, 2019 | 17 | 2019 |
| Automated valve detection in piping and instrumentation (P&ID) diagrams M Gupta, C Wei, T Czerniawski Proceedings of the 39th International Symposium on Automation and Robotics …, 2022 | 10 | 2022 |
| Automated 3D shape detection and outlier removal in cluttered laser scans of industrial assemblies M Nahangi, T Czerniawski, CT Haas Proc. of the International Conference of Innovation in Construction, 0-10, 2015 | 10 | 2015 |
| Current state of interface management in mega-construction projects S Shokri, S Ahn, T Czerniawski, CT Haas, SH Lee Construction Research Congress 2014: Construction in a Global Network, 2266-2275, 2014 | 10 | 2014 |
| Automated Wall Detection in 2D CAD Drawings to Create Digital 3D Models C Wei, M Gupta, T Czerniawski ISARC. Proceedings of the International Symposium on Automation and Robotics …, 2022 | 5 | 2022 |
| 3DFacilities T Czerniawski, F Leite Zenodo, 2018 | 5 | 2018 |
| Visual programming simulator for producing realistic labeled point clouds from digital infrastructure models K Korus, T Czerniawski, M Salamak Automation in Construction 156, 105126, 2023 | 4 | 2023 |
| Interoperability between deep neural networks and 3d architectural modeling software: Affordances of detection and segmentation C Wei, M Gupta, T Czerniawski Buildings 13 (9), 2336, 2023 | 3 | 2023 |
| Automated scan-to-building information modeling JW Ma, T Czerniawski, F Leite Research Companion to Building Information Modeling, 169-189, 2022 | 3 | 2022 |
