Reconstruction of Indoor Navigation Elements for Point Cloud of Buildings with Occlusions and Openings by Wall Segment Restoration from Indoor Context Labeling

Abstract

Indoor 3D reconstruction and navigation element extraction with point cloud data has become a research focus in recent years, which has important application in community refinement management, emergency rescue and evacuation, etc. Aiming at the problem that the complete wall surfaces cannot be obtained in the indoor space affected by the occluded objects and the existing methods of navigation element extraction are over-segmented or under-segmented, we propose a method to automatically reconstruct indoor navigation elements from unstructured 3D point cloud of buildings with occlusions and openings. First, the outline and occupancy information provided by the horizontal projection of the point cloud was used to guide the wall segment restoration. Second, we simulate the scanning process of a laser scanner for segmentation. Third, we use projection statistical graphs and given rules to identify missing wall surfaces and “hidden doors”. The method is tested on several building datasets with complex structures. The results show that the method can detect and reconstruct indoor navigation elements without viewpoint information. The means of deviation in the reconstructed models is between 0–5 cm, and the completeness and correction are greater than 80%. However, the proposed method also has some limitations for the extraction of “thick doors” with a large number of occluded, non-planar components.

Progressive Model-Driven Approach for 3D Modeling of Indoor Spaces

“…The level of detail is defined in five levels, from 0 to 5 (Figure 3) [27]. The reconstruction of the 3D model can be conducted simultaneously [2,9], or after labeling the interior spaces [15,18,23]. In some recent studies, the segmentation of spaces was discussed separately [4,24,25].…”

“…To conclude this section, gaps, noise and complexity of the point cloud, as well as the presence of interior furniture, should be considered as the main challenges of the building interior modeling process, which are still under discussion among researchers [23]. Our main goal in this study is to develop an innovative model-driven method to reduce the effect of the mentioned challenges in providing a solid, watertight 3D model of the interiors of multi-room environments.…”

Multistage Adaptive Point-Growth Network for Dense Point Cloud Completion

“…A complete and dense point cloud is the fundamental condition for 3D computer vision applications such as point cloud classification, segmentation and other 3D analysis and evaluation methods [11][12][13][14][15]. In practical applications, a dense and complete point cloud helps to better reconstruct the 3D model of a physical object, which can be better applied to industry and production [16,17]. Therefore, it is of great theoretical significance and application value to study the recovery of dense and complete point clouds from the observed incomplete point cloud data.…”

Labelled Indoor Point Cloud Dataset for BIM Related Applications

“…Developing automatic 3D reconstruction methodologies for creating 3D models of indoor spaces, which can be used for architectural design for example [46].…”