## 4 Performance Evaluation on Mobile Devices  
We present the performance evaluation of the silhouette mesh creation process conducted on various mobile devices. The devices used in the evaluation were Galaxy Note 10, Galaxy Tab s5e, Galaxy Note 9, and Galaxy S10. Our method was implemented using Unity _2017.04.10f1_ and ARCore SDK for Unity _1.5.0_.  
For the evaluation, we set a room with a zebra doll placed on the floor. A tripod was used to keep the position of the device during the evaluation. For each device, we mounted the device on the tripod and executed our AR application for three minutes.  
We measured image processing, segmentation, and silhouette mesh creation time respectively. The Image processing includes image acquisition and the conversion to the input format of the segmentation network. In detail, our process accesses the CPU of the device and obtains the raw image data using ARCore API. The raw image data is in the YUV-420-888 format with \(640\times 480\) pixels. This raw data is converted to the input format of the segmentation network model--RGB with \(192\times 192\) pixels. Up to here is the pre-processing in our evaluation. Once the converted data is fed into the segmentation module, the segmentation network runs on the mobile CPU to generate a segmentation map and classes of the recognized objects in the scene. Next, our system executes the 3D silhouette mesh creation procedure using the results from the segmentation module.  
The results of our performance evaluations are summarized in Table 2. On all tested devices, our entire process ran below 600 ms (roughly equivalent to about 20 frames). In the meantime, the positions of the created silhouette meshes are maintained and updated, as the camera position updated by ARCore. Thus, the camera rendering speed is maintained on average 30 fps.  
Pre-processing and segmentation times are not affected by the number of objects in the scene and the effect of the increased number of objects in silhouette creation time is insignificant. Moreover, our segmentation network is robust to the deformation of the object shape (Figure 6a) and works even with a complex background (Figure 6b). Note that we only used mobile CPU to achieve the reported performance, which means that GPU is still available to render a complex virtual environment. Thus, our silhouette mesh-based method has the potential to be integrated into various AR applications that need realistic interactions between real and virtual objects.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device Name** & **Image Processing** & **Segmentation** & **Silhouette Mesh** \\ \hline Galaxy Note 10 & 35 ms & 347 ms & 21 ms \\ Galaxy Tab s5e & 39 ms & 352 ms & 23 ms \\ Galaxy Note 9 & 42 ms & 465 ms & 21 ms \\ Galaxy S 10 & 44 ms & 572 ms & 26 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Processing time comparison on different mobile device performed with median average on 100 trials.  
Figure 6: Photo illustrations of our silhouette meshes generated from a single camera feed. Our proposed method is robust even in various orientation and deformable object (**a**), as well as in the complex background (**b**).