Door State Recognition Method for Wall Reconstruction from Scanned Scene in Point Clouds

Xiaojuan Ning, Zeqian Sun, Lanlan Wang, Man Wang, Zhiyong Lv, Jiguang Zhang () and Yinghui Wang
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Xiaojuan Ning: College of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Zeqian Sun: College of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Lanlan Wang: College of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Man Wang: College of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Zhiyong Lv: College of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Jiguang Zhang: National Laboratory of Pattern Recognition/National Laboratory of Multimodal Artificial Intelligence System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Yinghui Wang: School of Artificial Intelligence and Computer Science, Jiangnan University, 1800 of Lihu Road, Wuxi 214122, China

Mathematics, 2023, vol. 11, issue 5, 1-13

Abstract: Doors are important elements of building façades in scanned point clouds. Accurate door detection is a critical step in building reconstruction and indoor navigation. However, recent door detection methods may often obtain incomplete information and can only detect doors with a single state (open or closed). To improve this, a door state recognition method is proposed based on corner detection and straight-line fitting. Firstly, plane segmentation based on local features is introduced to obtain structural division from the raw scanned data to extract the wall. Next, the bounding box of each plane is calculated to obtain the corner points, which is then combined with the feature constraint to classify the elements of door and wall. Then, the boundary of each plane is extracted by normal vector, and the disordered and discontinuous boundary points are straight-line fitted based on projection. Finally, the state of the door is obtained through analysis of the angle between the straight-lines of the wall and the door. The effectiveness of the proposed method is tested and evaluated on the Livingroom of ICL-NUIM and House of Room detection datasets. Furthermore, comparative experimental results indicate that our method can extract corner points and recognize the different states of doors effectively and robustly in different scenes.

Keywords: point clouds; door state recognition; corner detection; boundary extraction; straight-line fitting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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