Fast and Automatic Reconstruction of Semantically Rich 3D Indoor Maps from Low-quality RGB-D Sequences  
[PERSON]  
1 Research Institute for Smart Cities & Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China; [EMAIL_ADDRESS] (S.T. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (R.G.) [PERSON]  
1 Research Institute for Smart Cities & Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China; [EMAIL_ADDRESS] (S.T. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (X.L. ); guorz@szu. The creation of a 3D indoor model involves measuring the geometric attributes of the original scanning data and transforming those measurements into a geometrically consistent and semantically rich representation [4]. Laser scanners are widely used for distance measurements of surfaces visible from the sensor's viewpoint. To obtain sufficient coverage of the spaces, a terrestrial laser scanner is placed in different locations throughout the facility and the point clouds from each station are merged into one via geometric registration. Consumer-level 3D acquisition devices such as mobile laser scanners and RGB-D sensors are now available for fast, accurate data collection [5; 6; 7; 8], and greatly reduce the time-cost of data collection [9; 10]. However, reconstruction of complex indoor scenes poses great challenges, and a method of automatic indoor modeling from point clouds is urgently needed. The main area of research interest is the problem of determining the architectural structure of indoor scenes (e.g., room spaces, walls, floors, ceilings, doors, and windows). Some applications such as path panning and indoor navigation require detailed relationships between build elements, such as wall connectivity or containing relationships between doors and walls. The process of converting point cloud data into a semantically rich 3D indoor model requires extensive use of computer vision techniques such as geometric segmentation and clustering, element labeling, and spatial relationship recovery [11; 12; 13]. Due to the complex structure layouts, clutter and occlusions, indoor reconstruction presents a numbers of distinctive challenges that make it significantly harder to manage than outdoor reconstruction [10; 14]. First, to handle such occluded areas, 3D scanners collect huge point clouds from close distances and multiple directions, and these often cause system slow-down or failure [15]. Second, the indoor reconstruction method must be highly tolerant of missing data from occlusions and clutter during data collection. This makes it hard to obtain the information about walls, floors, and other structures of interest. Furthermore, there is a challenge of recovering the interior structure and topology in terms of room space, connectivity between rooms, and the containing or adjacency relationships of different indoor components. Some research on indoor environments focuses on the classification and labeling of the objects with deep learning methods [16; 17], while the reconstruction of building components has been neglected. The conventional 3D modeling process requires modelers to subjectively determine building object shapes through manual creation, which leads to low productivity and erroneous modeling. To cope with unstructured and even incomplete point clouds, most reconstruction methods make use of \"PreDefined\" geometric knowledge of the interior entities to facilitate walls, floors, and ceiling recognition and reconstruction [1; 5; 18]. However, many existing solutions are targeted at single rooms or simply connected environments and cannot automatically reconstruct room spaces and their relationships in more complex environments [19; 20].