## 2 Related Work  
The proposed system uses simultaneous localization and mapping (SLAM) [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19] to generate point cloud information of the real environment for mapping. In [6], an extended Kalman filter (EKF) method was used in conjunction with a laser rangefinder and SLAM algorithm for use by a robot equipped with a monocular camera. In [7; 8; 9], multi-threading SLAM methods were used to divide tracking and mapping into two independent threads, while bundle adjustment (BA) was used to refine the accuracy of camera positions. In [10], Oriented Fast and Rotated BRIEF (ORB) feature detection was combined with additional threads for loop closure. In [11; 12], dense point clouds were used for tracking and mapping. That scheme is referred to as a direct SLAM algorithm. In [13], a direct method was combined with a non-direct method (feature-based method) to enhance computation speeds. The above studies focused on obtaining camera poses and improving SLAM computation speeds. Other studies [14; 15; 16; 17; 18; 19] investigated surface reconstruction from point clouds. Nevertheless, the aforementioned SLAM algorithms focused on mapping, the applications of which are limited. In this study, we sought to move beyond mere modeling of real environments. Our objective was to perform object segmentation in order to replace real-world entities with virtual replicas.  
Researchers dealing with object segmentation have applied object recognition to mapping results to segment scenes [20; 21; 22]. Computer-aided design (CAD) models can be used as an alternative to object recognition [22]. However, the a priori completion of models (via training) is not conducive to the segmentation of unknown objects. Methods that require the a priori establishment of datasets are also greatly limited in terms of applicability. Other segmentation methods, such as region growing ones [23; 24] are highly efficient. However, they require that users define the locations of seed points and tend to be susceptible to noise interference. The Hough transform [25] is highly robust against noise. However, it imposes a heavy computational burden. Thus, it is really only suitable for the segmentation of basic shapes, such as cylinders. Graph-based segmentation [26; 27] does not require the definition of seed points and is not limited in terms of object shape. Therefore, training is not required. However, all of the above-mentioned methods are applicable only to single frames or segmentation results pertaining to an environment that has already been modeled. These methods have not been applied to the segmentation of objects in multiple consecutive frames. The massive volume of information in real environments imposes a heavy computational burden, and segmentation cannot commence until the environment has been modeled entirely. In this study, we developed a segmentation process applicable to environments with multiple consecutive frames.  
Following the completion of object segmentation, the physical meaning of the information (e.g., shape and dimensions) must be defined to serve as the basis for virtual objects subsequently inserted into the scene. In this study, we employed shape fitting to derive the shapes, dimensions, positions, and directions of segmentation point groups in order to create virtual objects that are representative of the real-world entities on which they were modeled. In [28; 29], random sample consensus (RANSAC) and mean shift methods were used to identify objects and poses by group. However, these methods depend heavily on information from two-dimensional (2D) images and require off-line training. In [30; 31], superquadrics were used to obtain shape information to avoid the need for off-line training by deriving shape information from three-dimensional (3D) information. In this study, we also adopted superquadrics to derive the shape of object point groups.