| id |
ecaade2024_201 |
| authors |
Hashizume, Keiji; Fukuda, Tomohiro; Yabuki, Nobuyoshi |
| year |
2024 |
| title |
A Surface Modeling Method for Indoor Spaces from 3D Point Cloud Reconstructed by 3D Gaussian Splatting |
| 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. 695–704 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.695
|
| summary |
Building information modeling (BIM) is becoming increasingly important in architectural projects, and the implementation of BIM in new construction projects is progressing. On the other hand, many existing buildings do not have BIM data, so it is necessary to create it from scratch. A common method for converting existing buildings to BIM is scan-to-BIM, using techniques such as laser scanning or photogrammetry. However, laser scanning provides accurate point cloud data but requires expensive equipment, while photogrammetry is generally cost-effective but has lower accuracy point cloud data. Another approach for creating BIM from 2D images is to use neural radiance fields (NeRF). However, NeRF faces challenges in terms of data accuracy and processing speed when dealing with large or complex scenes. In contrast, 3D Gaussian Splatting is an emerging computer vision technology that uses machine learning to reconstruct 3D scenes from 2D images faster than NeRF, with comparable or better quality. Therefore, this study proposes a method to create surface models consisting of floors, walls, and ceilings as a preliminary step to creating BIM data for existing indoor spaces using 3D Gaussian Splatting. First, point cloud is generated using 3D Gaussian Splatting, followed by noise reduction. The point cloud is then classified based on height. Subsequently, processing such as extraction of boundary primitives from the point cloud of the floor and classification of feature points are performed to estimate the shape of the floor. Finally, ceilings and walls are created based on height and floor shape. The results of validation confirm an error of between 0.01m and 0.5m in the generated surface models. This study proposes a novel attempt to create 3D models using 3D Gaussian Splatting, contributing to the generation of BIM data for existing buildings. |
| keywords |
Point Cloud, 3D Gaussian Splatting, Scan2BIM, Surface Modeling, Indoor 3D Reconstruction |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (2,350,110 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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