TY - GEN
T1 - After Abstraction, before Figuration
AU - Tu, Chun Man
AU - Hou, June Hao
N1 - Publisher Copyright:
© 2020 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/8
Y1 - 2020/8
N2 - In the era of three-dimensional (3D) informatics, the 3D point cloud modeling algorithm has the potential to further develop. In this study, we attempt to eliminate the limitations of the traditional reverse modeling method and directly turn point cloud data into the material for innovative architectural design by integrating 3D point cloud modeling into the CAD/CAM platform(Rhino/Grasshopper) most widely used by parametric designers. In this way, the randomly ordered point cloud model can be regenerated and reordered according to the designer's requirements. In addition, point cloud data can be spatially segmented and morphologically evolved according to the designer's preferences to construct a 3D model with higher efficiency and more dynamic real-time adjustment compared with the triangular mesh model. Moreover, when a computer vision technique is integrated into the point cloud design process, the point cloud model can be further used to more efficiently achieve rapid visualization, artisticization, and form adjustment. Therefore, point cloud modeling can not only be applied to the spatial structure presentation of building information modeling(BIM) but also can provide further opportunities for creative architectural design.
AB - In the era of three-dimensional (3D) informatics, the 3D point cloud modeling algorithm has the potential to further develop. In this study, we attempt to eliminate the limitations of the traditional reverse modeling method and directly turn point cloud data into the material for innovative architectural design by integrating 3D point cloud modeling into the CAD/CAM platform(Rhino/Grasshopper) most widely used by parametric designers. In this way, the randomly ordered point cloud model can be regenerated and reordered according to the designer's requirements. In addition, point cloud data can be spatially segmented and morphologically evolved according to the designer's preferences to construct a 3D model with higher efficiency and more dynamic real-time adjustment compared with the triangular mesh model. Moreover, when a computer vision technique is integrated into the point cloud design process, the point cloud model can be further used to more efficiently achieve rapid visualization, artisticization, and form adjustment. Therefore, point cloud modeling can not only be applied to the spatial structure presentation of building information modeling(BIM) but also can provide further opportunities for creative architectural design.
KW - Computer vision
KW - Point set registration
KW - Regeneration
KW - Three-dimensional point-cloud model
KW - Topology optimization
UR - http://www.scopus.com/inward/record.url?scp=85091696655&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85091696655
T3 - RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2020
SP - 519
EP - 528
BT - RE
A2 - Holzer, Dominik
A2 - Nakapan, Walaiporn
A2 - Globa, Anastasia
A2 - Koh, Immanuel
PB - The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)
T2 - 25th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2020
Y2 - 5 August 2020 through 6 August 2020
ER -