Díaz-Vilariño Lucía, Khoshelham Kourosh, Martínez-Sánchez Joaquín, Arias Pedro
Applied Geotechnologies Research Group, University of Vigo. Rúa Maxwell s/n, Campus Lagoas-Marcosende, Vigo 36310, Spain.
Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, Enschede 7514 AE, The Netherlands.
Sensors (Basel). 2015 Feb 3;15(2):3491-512. doi: 10.3390/s150203491.
3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.
由于室内环境的3D模型可应用于广泛的领域，如重新设计、可视化、监测和模拟，其重要性日益凸显。这些模型通常仅存在于新建建筑中；因此，开发从图像和/或点云自动重建室内3D模型的方法可以使这个过程更轻松、快速且成本更低。在定义建筑内部的构造元素中，门是非常常见的元素，其检测对于了解环境结构、进行高效导航或规划合适的疏散路线非常有用。门通过与墙壁共面在拓扑上相连，再加上室内不可避免地存在杂物和遮挡，增加了识别过程自动化的固有复杂性。在这项工作中，我们提出了一套基于点云和图像的用于建筑内部重建和解释的技术流程。该方法分析了室内环境的可见性问题，并深入研究了门候选检测。所提出的方法在真实数据集上进行了测试，显示出其具有高门检测率的潜力，以及对稳健高效的围护结构重建的适用性。
