TY - GEN
T1 - Semantic-Rich 3D CAD models for built environments from point clouds
T2 - 2017 ASCE International Workshop on Computing in Civil Engineering, IWCCE 2017
AU - Perez-Perez, Yeritza
AU - Golparvar-Fard, Mani
AU - El-Rayes, Khaled
N1 - Publisher Copyright:
© 2017 American Society of Civil Engineers.
PY - 2017
Y1 - 2017
N2 - The last few years has been subject to an unprecedented growth in the application of 3D data for representing built environment and performing engineering analyses such as site planning, and condition assessment. Despite the growing demand, generating semantic rich 3D CAD models, particularly when building and structural systems are exposed remains a labor-intensive and time-consuming process. To address these limitations, this paper presents an end-to-end procedure to produce semantic rich 3D CAD models from point cloud data at a user defined level of abstraction. The procedure starts by segmenting a point cloud while considering local context using a multi-scale region growing algorithm. A Markov-Random-Field optimization labels segments based on their semantic categories. This step reduces the over-segmentation produced during the segmentation stage by compositing similarly labeled segments into super segments. The interconnectivity among these super-segments are reasoned and b-splines and solid geometrical representations are fit to produce 3D NURBS surfaces and cylindrical elements, respectively. Experimental results on real-world point clouds show an average fit error of 6.33 E-01 mm making the method the first to include beams and columns in an automated Scan2BIM process.
AB - The last few years has been subject to an unprecedented growth in the application of 3D data for representing built environment and performing engineering analyses such as site planning, and condition assessment. Despite the growing demand, generating semantic rich 3D CAD models, particularly when building and structural systems are exposed remains a labor-intensive and time-consuming process. To address these limitations, this paper presents an end-to-end procedure to produce semantic rich 3D CAD models from point cloud data at a user defined level of abstraction. The procedure starts by segmenting a point cloud while considering local context using a multi-scale region growing algorithm. A Markov-Random-Field optimization labels segments based on their semantic categories. This step reduces the over-segmentation produced during the segmentation stage by compositing similarly labeled segments into super segments. The interconnectivity among these super-segments are reasoned and b-splines and solid geometrical representations are fit to produce 3D NURBS surfaces and cylindrical elements, respectively. Experimental results on real-world point clouds show an average fit error of 6.33 E-01 mm making the method the first to include beams and columns in an automated Scan2BIM process.
UR - http://www.scopus.com/inward/record.url?scp=85021698800&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021698800&partnerID=8YFLogxK
U2 - 10.1061/9780784480823.021
DO - 10.1061/9780784480823.021
M3 - Conference contribution
AN - SCOPUS:85021698800
SN - 9780784480823
T3 - Congress on Computing in Civil Engineering, Proceedings
SP - 166
EP - 174
BT - Computing in Civil Engineering 2017
A2 - Lin, Ken-Yu
A2 - Lin, Ken-Yu
A2 - El-Gohary, Nora
A2 - El-Gohary, Nora
A2 - Tang, Pingbo
A2 - Tang, Pingbo
PB - American Society of Civil Engineers
Y2 - 25 June 2017 through 27 June 2017
ER -