| id |
caadria2024_414 |
| authors |
Uzun, Fatih, Altun, Sevgi, Kayasü, Sena, Öztürk, Berkay, Şahin, Yusuf, Ünal, Gözde and Özkar, Mine |
| year |
2024 |
| title |
Utilizing UV-Mapping for the 3D-Point Cloud Segmentation of Architectural Heritage |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 313322 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.313
|
| summary |
This paper presents a procedural framework to process raw photogrammetric data intended for 3D point cloud segmentation of masonry structures. Raw data is segmented in order to obtain operable 3D models through processes that increasingly integrate computational methods, which can improve efficiency and accuracy. The idea is to improve the quality of the initial data, which can then be used to train a machine-learning system in identifying these materials more accurately. The approach incorporates a high-poly detailed mesh model generated through photogrammetry. The detailed model serves as a reference to extract colour information that we project onto a custom-created, low-poly representation of the dome architectural element, ensuring a precise fit with the target model. The model to utilise UV maps and height maps to preprocess data across various scales is a step towards facilitating the documentation and conservation of historic structures with an awareness of architectural knowledge. |
| keywords |
architectural heritage, unit-based, masonry documentation, 3D point cloud segmentation, UV mapping, point cloud processing |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (2,063,626 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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