TY - GEN
T1 - 3DFacilities
T2 - 25th Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2018
AU - Czerniawski, Thomas
AU - Leite, Fernanda
N1 - Funding Information:
Acknowledgments. This research was supported, in part, by the National Science Foundation (NSF) under award number 1562438. Their support is gratefully acknowledged. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Mention of trade names in this article does not imply endorsement by the University of Texas at Austin or NSF.
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Scan-to-BIM is the process of converting 3D reconstructions into building information models (BIM). Currently, it involves manual tracing of point clouds by human users in BIM authoring tools, with some automation functionality available for walls, floors, windows, doors, and piping. Emerging semantic segmentation methods demonstrate a level of versatility that could extend the capabilities of automated Scan-to-BIM well past the limited existing object categories. The accuracy of supervised deep learning methods in the context of 3D scene segmentation has experienced rapid improvement over the past year due to the recent availability of large, annotated datasets of indoor spaces. Unfortunately, the semantic object categories in the available datasets do not cover many essential BIM object categories, such as heating, ventilation and air-conditioning (HVAC), and plumbing systems. In an effort to leverage the success of deep learning for Scan-to-BIM, we present 3DFacilities, an annotated dataset of 3D reconstructions of building facilities. The dataset contains over 11,000 individual RGB-D frames comprising 50 scene reconstructions annotated with 3D camera poses and per-vertex and per-pixel annotations. Our dataset is available at https://thomasczerniawski.com/3dfacilities/.
AB - Scan-to-BIM is the process of converting 3D reconstructions into building information models (BIM). Currently, it involves manual tracing of point clouds by human users in BIM authoring tools, with some automation functionality available for walls, floors, windows, doors, and piping. Emerging semantic segmentation methods demonstrate a level of versatility that could extend the capabilities of automated Scan-to-BIM well past the limited existing object categories. The accuracy of supervised deep learning methods in the context of 3D scene segmentation has experienced rapid improvement over the past year due to the recent availability of large, annotated datasets of indoor spaces. Unfortunately, the semantic object categories in the available datasets do not cover many essential BIM object categories, such as heating, ventilation and air-conditioning (HVAC), and plumbing systems. In an effort to leverage the success of deep learning for Scan-to-BIM, we present 3DFacilities, an annotated dataset of 3D reconstructions of building facilities. The dataset contains over 11,000 individual RGB-D frames comprising 50 scene reconstructions annotated with 3D camera poses and per-vertex and per-pixel annotations. Our dataset is available at https://thomasczerniawski.com/3dfacilities/.
KW - Building information modeling
KW - Computer vision
KW - Deep learning
UR - http://www.scopus.com/inward/record.url?scp=85049116405&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049116405&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91635-4_10
DO - 10.1007/978-3-319-91635-4_10
M3 - Conference contribution
AN - SCOPUS:85049116405
SN - 9783319916347
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 186
EP - 200
BT - Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings
A2 - Domer, Bernd
A2 - Smith, Ian F.
PB - Springer Verlag
Y2 - 10 June 2018 through 13 June 2018
ER -