## 6 Conclusions  
In this article, a planar pattern-based MAR prototype, named ModAR, is presented. It targets low-end mobile devices which are not certified or cannot enable AR technology, as they lack computational power, compatible operating system version and/or specific hardware. This user group is rarely considered as a target for AR development. Several deployment scenarios were implemented to detect the optimized computer vision and rendering methodology for the developed prototype. The pose of the 3D models proved to be well aligned with the pattern image for all tested detectors-descriptors combinations, identifying only minor misalignments after a certain camera distance from the object. In response to the lack of commercial and research solutions to limited resources, ModAR attains good precision in positioning and high frame rates. ModAR does not exceed the size of 400 KB and the evaluation tests on low-end and mid-range smartphones demonstrate that it achieves 93% pattern recognition accuracy and thus accurate camera pose estimation for camera frames where the resolution of the pattern in the frame is 0.5 to 0.9 times the resolution of the pattern image, as well as 100% pattern recognition accuracy for camera frames where the resolution of the depicted pattern is 0.9 to 5 times the resolution of the pattern image, regardless of any in-plane rotation. The oriented FAST detector with an upper threshold of 2000 feature points per frame combined with the ORB descriptor were used in the prototype, as they proved to yield the best results in terms of robustness and efficiency. The experiments showed that while the use of the AGAST-BRISK combination of detector-descriptor improves the recognition accuracy by 2-4% (depending on the parameterization used), the loss in speed is dramatical, as the FAST-ORB combination achieves 90% reduction in image matching time compared to the time required by the AGAST detector and the BRISK descriptor. In turns, performance analysis and extensive benchmarking on the graphics techniques and strategies compatible with the OpenGL ES 2.0 showcased the efficacy of the developed 3D engine and rendering scheme for large vertex data. In less than 30 s and with an average stable frame rate of 20 FPS for low-specification devices, a medium sized textured 3D model is drawn and overlaid the real scene. The AR experience is delightful at no expense of visual quality, even for large volume data, exceeding 7 M vertices. The proposed combination of VBO data grouping, geometric instancing, sub-texturing as well as vertex data types simplification and frustrum culling, can speed up graphics processing of OBJ files up to 3\(\times\) compared to standard rendering methods. The developed compute system is able to identify and estimate the pose of the 3D models in various situations (indoors/outdoors) depending on the input pattern image, thus allowing the methodologies employed in this work to be further extended or optimized. Future work involves further optimization of the presented workflow for acceleration of the AR augmentation; investigation into fully-automatic camera calibration of the mobile devices; further experiments of the ModAR prototype to augment outdoor CH sites with high-resolution 3D models; and performance of a subjective study in order to understand and quantify user experience as well as to evaluate the quality of service. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rendering Performance** & \multicolumn{2}{c}{**POTTERY**} & \multicolumn{2}{c}{**STATUE**} \\ \cline{2-5}
**on Mid-Range** & **FPS** & **Time (s)** & **FPS** & **Time (s)** \\ \hline Index Rendering + Arrays & 32 & 43.95 & 27 & 78.12 \\ Index Rendering + VBOs & 46 & 17.99 & 43 & 24.18 \\ Instancing + Arrays & 42 & 20.04 & 40 & 36.30 \\ Instancing + VBOs & 53 & 12.56 & 57 & 13.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Average frame rate and total rendering time of ten meshes of POTTERY and STATUE using a combination of rendering techniques and buffer storage on the mid-range device.