<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, a 3D model based on semantic annotation is constructed with the goal of improving the efficiency of building interior design. By analyzing the basic methods of semantic annotation for building interiors, the method of positioning and map construction is selected to obtain the indoor point cloud data. The distance between 3D spatial lines is calculated using the frame line extraction algorithm, and the target area of the frame line candidate is divided according to the distance. According to the principle of detecting raster circles using the Hough transform, an interior design structure recognition method is proposed for recognizing windows, doors, and walls in building interiors. The results show that the modeling time of the semantically annotated 3D model is 10 seconds faster than the other models on the wall; 9 seconds are saved on the door modeling, and 7 seconds are saved on the window modeling. The visualization effect of semantically annotated 3D models is mostly concentrated in (0.5-1), and a large number of data points are distributed in (0.6-0.9), which indicates that the visualization effect of semantically annotated 3D models is better. The semantically annotated 3D model proposed in this paper can improve the visualization of architectural interior design, which can improve the efficiency of designers to a certain extent.</p>
</abstract>

In this paper, a 3D model based on semantic annotation is constructed with the goal of improving the efficiency of building interior design. By analyzing the basic methods of semantic annotation for building interiors, the method of positioning and map construction is selected to obtain the indoor point cloud data. The distance between 3D spatial lines is calculated using the frame line extraction algorithm, and the target area of the frame line candidate is divided according to the distance. According to the principle of detecting raster circles using the Hough transform, an interior design structure recognition method is proposed for recognizing windows, doors, and walls in building interiors. The results show that the modeling time of the semantically annotated 3D model is 10 seconds faster than the other models on the wall; 9 seconds are saved on the door modeling, and 7 seconds are saved on the window modeling. The visualization effect of semantically annotated 3D models is mostly concentrated in (0.5-1), and a large number of data points are distributed in (0.6-0.9), which indicates that the visualization effect of semantically annotated 3D models is better. The semantically annotated 3D model proposed in this paper can improve the visualization of architectural interior design, which can improve the efficiency of designers to a certain extent.

Visualization analysis of architectural interior design combined with virtual reality technology under new process conditions

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, a 3D model based on semantic annotation is constructed with the goal of improving the efficiency of building interior design. By analyzing the...

Visualization analysis of architectural interior design combined with virtual reality technology under new process conditions

Online veröffentlicht: 05. Dez. 2023

Seitenbereich: -

Eingereicht: 01. Feb. 2023

Akzeptiert: 15. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01345

Schlüsselwörter
Semantic annotation, Frame line extraction, Interior structure recognition, 3D modeling, Design visualization

© 2023 Xiang Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Visualization analysis of architectural interior design combined with virtual reality technology under new process conditions

Online veröffentlicht: 05. Dez. 2023

Seitenbereich: -

Eingereicht: 01. Feb. 2023

Akzeptiert: 15. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01345

SchlüsselwörterSemantic annotation, Frame line extraction, Interior structure recognition, 3D modeling, Design visualization

© 2023 Xiang Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Semantic annotation, Frame line extraction, Interior structure recognition, 3D modeling, Design visualization