The integration of Building Information Modelling (BIM) and geographic information systems (GIS) is a promising but challenging topic to solve problems in construction industry. However, loading and rendering rich BIM geometric data and large-scale GIS spatial information in a unified system is still technologically challenging. Current efforts mainly simplify the geometry in BIM models, or convert BIM geometric data to a lower level of detail (LOD). By noticing that only exterior features of BIM models are visible from outdoor observation points, culling BIM interior facilities can dramatically reduce the computational burden when visualizing BIM models in GIS. This study explores the outline detection problem and presents the OutDet algorithm, which selects representative observation points, transforms & projects the BIM geometric data into the same coordinate system, and detects the visible facilities. Empirical study results show that OutDet can cull a large portion of unnecessary features when rendering BIM models in GIS. The use of outlines of BIM models is not an alternative but rather a supplementary approach for current solutions. Jointly using LOD and outer surface can help improve the efficiency of integrated BIM-GIS visualization.Because OutDet retains BIM geometry and semantics, it can be applied to more BIM-GIS applications..

Recently, researchers have realized a number of achievements involving deep-learning-based neural networks for the tasks of segmentation and detection based on 2D images, 3D point clouds, etc. Using 2D and 3D information fusion for the advantages of compensation and accuracy improvement has become a hot research topic. However, there are no critical reviews focusing on the fusion strategies of 2D and 3D information integration based on various data for segmentation and detection, which are the basic tasks of computer vision. To boost the development of this research domain, the existing representative fusion strategies are collected, introduced, categorized, and summarized in this paper. In addition, the general structures of different kinds of fusion strategies were firstly abstracted and categorized, which may inspire researchers. Moreover, according to the methods included in this paper, the 2D information and 3D information of different methods come from various kinds of data. Furthermore, suitable datasets are introduced and comparatively summarized to support the relative research. Last but not least, we put forward some open challenges and promising directions for future research.