## 2 Related Work
extend this approach so that it is capable of identifying and modeling the main visible structural opening detection components of an indoor environment including walls, floors, ceilings, windows, and doorways [2]. [PERSON] et al. proposed a volumetric, parametric building model by also incorporating contextual information [14]. A global optimization approach was used to reconstruct of wall elements shared between rooms while simultaneously maintaining plausible connectivity between all wall elements. [PERSON] et al. first proposed an occlusion-aware method to extract candidate wall elements while taking into account possibly occluded parts of the surfaces to determine real wall heights for filtering out invalid candidates [9]. The projected 2D lines of candidate wall patches corresponding to the same wall structure were merged and a 2D cell complex was obtained from the intersection of the remaining lines. By computing the diffusion distances between faces, a global measure of affinity was used to iteratively cluster the cell complex into individual rooms. However, this method does not attempt to recognize opening structures and the relationships between these indoor elements. The above discussion shows that the constraints of automatic indoor reconstruction are due to either missing data due to occlusions and clutter, or a lack of robust space partitioning. Because consumer-level 3D acquisition devices such as RGB-D sensors allow more convenient interoperates and are capable of obtaining additional valid information such as camera trajectories, frame sequences with timestamps, and colorized point clouds that provide new opportunities to integrate multiple types of data for indoor reconstruction. [PERSON] et al. proposed a robust CPU-based approach to reconstruct indoor scenes with a consumer RGB-D camera. The method combines feature-based camera tracking and volumetric-based data together. The experiment results demonstrate a good reconstruction performance in terms of both robustness and efficiency [31]. Similarly, a robust approach to elaborately the indoor environment with a consumer depth camera is proposed and the main contribution of this research is using the local-to-global registration to obtain complete scene reconstruction [32]. Besides, to enhance the robustness of RGB-D mapping, a multi-camera dense RGB-D SLAM system is proposed and the experimental results shows the multi-camera system is able to increase the efficiency and improve the accuracy significantly [33]. [PERSON] at.al uses a method to automatically model indoor scenes based on low-quality RGB-D sequences by establishing the relationships between objects and knowledge from the model database [34]. Based on large-scale RGB-D datasets, [PERSON] et al. use the network architecture of the 3D Network-in-Network [35] for 3D object classification and conducts semantic labeling by extending the semantic segmentation method [36] to 3D [17]. However, these two methods focus on the reconstruction of indoor furniture rather than indoor structural components. Systematic indoor reconstruction methods based on RGB-D mapping systems are rarely investigated. In this study, we seek to establish a semi-automatic indoor reconstruction method by incorporating multiple types of data from low-quality RGB-D sequences. This paper presents the following novel findings. First, the proposed indoor reconstruction makes full use of the camera trajectory and semantic labeling tags of RGB-D frames except colorized point cloud, thus improving robustness and recognition accuracy for indoor reconstruction. Second, inevitable mapping errors can result in inconsistency between the planes detected from different functional spaces, for instance the joint wall between two spaces, an opening and its connected wall. A global optimization approach is designed to eliminate inconsistency between the adjacent walls, walls, and openings and maintain plausible connectivity between the walls and the relationships between the walls and openings.