Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
