{"title":"From PC2BIM: Automatic Model generation from Indoor Point Cloud","authors":"Danielle Tchuinkou Kwadjo, Erman Nghonda Tchinda, C. Bobda, Nareph Menadjou, Cedrique Fotsing, Nafissetou Nziengam, Nafissetou Nziengam","doi":"10.1145/3349801.3349825","DOIUrl":null,"url":null,"abstract":"In this paper, we present a system to automatically generate BIMs1 model from indoor point cloud. In contrary to previous works, our approach is able to take as input a point cloud with the minimum of information namely the points of coordinates (x, y, z) and produce excellent results. We first detect major flat surfaces such a walls, floor, and ceiling which are the bedrocks of our structure. Then, we present a novel 2D matrix template representation of walls which ease the operations like room layout and openings detection in polynomial time. Finally, we generate the BIM model rich with spatial and semantic information about the physical structures. A series of experiments performed show the efficiency and the precision of our approach.","PeriodicalId":299138,"journal":{"name":"Proceedings of the 13th International Conference on Distributed Smart Cameras","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 13th International Conference on Distributed Smart Cameras","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3349801.3349825","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this paper, we present a system to automatically generate BIMs1 model from indoor point cloud. In contrary to previous works, our approach is able to take as input a point cloud with the minimum of information namely the points of coordinates (x, y, z) and produce excellent results. We first detect major flat surfaces such a walls, floor, and ceiling which are the bedrocks of our structure. Then, we present a novel 2D matrix template representation of walls which ease the operations like room layout and openings detection in polynomial time. Finally, we generate the BIM model rich with spatial and semantic information about the physical structures. A series of experiments performed show the efficiency and the precision of our approach.
本文提出了一种基于室内点云的BIMs1模型自动生成系统。与以往的工作相反,我们的方法能够以最小的信息即坐标(x, y, z)点作为输入点云,并产生出色的结果。我们首先检测主要的平面,如墙壁、地板和天花板,它们是我们结构的基石。然后,我们提出了一种新的二维矩阵模板表示墙,简化了多项式时间内房间布局和开口检测等操作。最后,我们生成了具有丰富的物理结构空间和语义信息的BIM模型。一系列的实验证明了该方法的有效性和精确性。