| id |
ecaade2024_223 |
| authors |
Kim, Taehoon; Kim, Geunjae; Hong, Soon Min; Choo, Seungyeon |
| year |
2024 |
| title |
Development of Structure-Specific Architectural BIM Object Automatic Generation Technology for Reverse Design Based on Deep Learning |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.705
|
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 705–714 |
| summary |
This research developed a technology for classifying architectural objects based on point cloud data and creating Building Information Modeling (BIM) models in the reverse engineering process. This research analyzed the limitations in the process and current advancements in point cloud-based object recognition and classification technology, leveraging semantic segmentation. The classification method employed a semantic segmentation-based network to classify objects into desired classes within 3D point cloud data. Specifically, the TD3D network, known for its superior performance, was utilized in this study, with publicly available datasets used for training. Moreover, the developed algorithm for creating architectural object BIM models was specifically designed based on the simplest structure and form, namely reinforced concrete structure. In conclusion, the study aimed to develop technology more aligned with the fundamental purpose of performing reverse engineering in an architectural context. Analysis of validated architectural structures revealed that, despite deviating from actual measurement times, concrete-reinforced structures demonstrated the highest performance. |
| keywords |
Reverse engineering, Deep Learning, Point Cloud, Automatic object generation, BIM |
| series |
eCAADe |
| email |
Choo@knu.ac.kr |
| full text |
 file.pdf (2,264,158 bytes) |
| references |
Content-type: text/plain
|
| last changed |
2024/11/17 22:05 |
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