Automatic reconstruction of parametric building models from indoor point clouds
Automatic reconstruction of parametric building models from indoor point clouds
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DOI：
10.1016/j.cag.2015.07.008
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发表时间：
2016-02-01
影响因子：
2.5
通讯作者：
Klein, Reinhard
中科院分区：
文献类型：
--
作者：
Ochmann, Sebastian;Vock, Richard;Klein, Reinhard
提出了一种从室内点云数据自动重建三维建筑模型的方法。虽然最近在这一领域开发的方法专注于仅仅是局部表面重建，从而实现高效的可视化，但我们的方法的目标是体积、参数建筑模型，该模型另外结合了上下文信息，如全球墙的连通性。与纯粹的表面重建相比，我们的表示允许更全面的使用：首先，它支持高效的高级编辑操作，例如墙壁移除或房间重塑，这些操作总是导致拓扑一致的表示。其次，它可以方便地进行测量，例如确定墙壁厚度或房间面积。这些特性使得我们的重建方法特别有利于建筑师或工程师进行规划、翻新或翻新。借鉴已有方法的思想，将重建问题转化为一个标号问题，用能量最小化的方法来解决。这种全局优化方法允许重建房间之间共享的墙元素，同时保持所有墙元素之间的合理连接。自动将点云分割成房间和外部区域，过滤大范围的离群值，并产生用于定义能量最小化的标记成本的先验。重建的模型进一步丰富了检测到的门窗。我们展示了我们的新方法在各种复杂的真实世界数据集上的适用性和重建能力，这些数据集只需要很少的参数调整或不需要参数调整。(C)2015年提交人。爱思唯尔有限公司出版。
We present an automatic approach for the reconstruction of parametric 3D building models from indoor point clouds. While recently developed methods in this domain focus on mere local surface reconstructions which enable e.g. efficient visualization, our approach aims for a volumetric, parametric building model that additionally incorporates contextual information such as global wall connectivity. In contrast to pure surface reconstructions, our representation thereby allows more comprehensive use: first, it enables efficient high-level editing operations in terms of e.g. wall removal or room reshaping which always result in a topologically consistent representation. Second, it enables easy taking of measurements like e.g. determining wall thickness or room areas. These properties render our reconstruction method especially beneficial to architects or engineers for planning renovation or retrofitting. Following the idea of previous approaches, the reconstruction task is cast as a labeling problem which is solved by an energy minimization. This global optimization approach allows for the reconstruction of wall elements shared between rooms while simultaneously maintaining plausible connectivity between all wall elements. An automatic prior segmentation of the point clouds into rooms and outside area filters large-scale outliers and yields priors for the definition of labeling costs for the energy minimization. The reconstructed model is further enriched by detected doors and windows. We demonstrate the applicability and reconstruction power of our new approach on a variety of complex real-world datasets requiring little or no parameter adjustment. (C) 2015 The Authors. Published by Elsevier Ltd.