# Real-Time 3D Reconstruction Method for Holographic Telepresence
## 4 Results and Discussion  
This section explains the experimental setup for real-time 3D reconstruction for 3D telepresence. The test application of our work will be further explained in this section as well. The experimental setup is presented in Figure 8a, which shows equipment such as desktop computers, a Kinect and a tripod, as well as its placement used for data acquisition of the local user in Figure 8b. An RGB-D camera was placed with appropriate height, \(h\) at the distance, \(d\), for enabling data acquisition using the Kinect V2 to gather the depth image and colour image of the depth used for life-sized 3D reconstruction. The depth camera field of view (FOV), \(\alpha^{\circ}\) will be set to a suitable angle to enable the life-size capture of the local user's full body. The Z-hologram is built with several components: a monitor screen, Perspex board, PVC pipe, and plywood. Firstly, the hologram is built with the Perspex board according to the measurement of the monitor screen such that the holographic content fits onto the Perspex board. The monitor then projects the reconstructed user onto the Perspex board. The reconstructed user appeared in the real environment when the projection from the monitor was reflected on the Perspex. The Plywood board is used to support the monitor placed on the board such that the monitor is stable on the Perspex and acts as the wall structure for the hologram. The result is shown in Figure 9.  
Figure 7: The reflective ray principle of the hologram. A test application has been prepared to integrate the real-time 3D reconstruction with 3D telepresence. First, both local and remote users are required to set roles as either sender or receiver using the simple user interface (UI) of the application. Figure 8: The experimental setup. (**a**) The experimental setup using two Kinect. (**b**) The setup with user. Figure 9: The Z-Hologram that was built for this research. As explained in the previous section, the research framework has a few phases, including 3D reconstruction and 3D telepresence. The results for each process of the 3D reconstruction using the marching cube algorithm to a produced textured 3D model of the local user are as illustrated in Figure 10. The first stage generates depth streams during data acquisition from the RGB-D sensor. Data acquisition was capturing and processing data input from each of the RGB-D cameras on the client site. The captured data will later be transmitted to the server using a stable network with background removal. During point cloud extraction, the data is captured from multiple sensors. After projecting from depth field to point cloud, a virtual scene is reconstructed from a sequence of point clouds. This process is called point cloud registration, where the algorithm has been used to estimate the rigid transformation between the two datasets of point clouds, which can then be used to merge the two sources of point clouds. This registration process can then extract objects and transform the point cloud data into surface information to recreate a 3D mesh. Up to the surface generation stage, we have confirmed that the application was still running in real-time. Based on Figure 10, the background removal has been using a depth map, and the foreground pixels were extracted when the foreground or background information was converted into binary ones and zeros, respectively. The corresponding point set estimation algorithm fused the point cloud projected using the filtered depth map captured from the RGB-D sensor. During surface generation using the Marching Cube algorithm, the voxel grid used was set to the size of the depth frame height and width before re-evaluating the grid, calculating each square to find acceptable triangles to generate the surface. Then, the attempt to create the calculated texture map was initiated for the texture mapping process. In Table 1 we have presented the processing time measurement for the real-time process except for UV mapping. Table 1 shows the results we have compared to previous research.