# Performance Evaluation of Ground AR Anchor with WebXR Device API
## 4 Results and Discussion
### Discussion  
The experimental results conducted in this paper were conducted with the ground environment, the 3D model to be loaded, and the device environment for measurement fixed. In order to compare the performance according to the difference in the ground environment, the experiment was divided into the ground with strong reflection and the ground with weak reflection. In addition, performance differences according to environments were analyzed by conducting the same experiment in different ground environments.  
In the case of the camera trajectory experiment, a 3D model was loaded in the WebXR environment while the HMD was used as ground truth and the mobile device was fixed, and the fixed device was simultaneously moved by the X, Y, and Z axes. Experiments were conducted for each axis to improve the accuracy of the amount of change when moving in 3D space based on the anchor. In the case of an HMD used as ground truth, the head tracking function can be used to more accurately check the result value of the movement amount when the device is moved. Through this, an experiment was conducted by designating an anchor and loading a 3D model from a mobile device at the same location as the head tracker. Even if the experiment is conducted as in this paper, if the accuracy of the anchor is inferior, when the device is moved after loading the 3D model, a difference in the amount of movement may occur in the mobile device compared to the ground truth. In order to clearly confirm the result of this difference, the standard deviation of the moving amount was calculated in this paper.  
Figure 10: 3D model rendering FPS of WebXR.  
In the case of the difference image experiment, the device was moved to the left and right while the horizontal and viewpoint were fixed. The movement to each side was designated as an angular position based on a straight line connecting the position of the front and the anchor vertically. Ground images from each direction, images with only 3D models loaded, images with only real models fixed, and images loaded with both real and 3D models were captured. If the anchor's accuracy is poor, we can check if the result value is not black (#000000) when calculating the difference image of the captured image. In addition, in this paper, the reliability evaluation method used in the image processing field was used to improve the reliability of the accuracy of the performance evaluation. The average absolute error and IoU were used to express the result mathematically and to obtain the error by pixel and by region, respectively.  
Among the methods used, the mean absolute error was used to check the error mean for the pixel values of the difference image. The normalization value of the mean absolute error can be confirmed as the same result as using the mean absolute percentage error (MAPE) method. Additionally, IoU was used to check the match rate for the area of the difference image. In the image processing field, there are various methods for evaluating the reliability of the error rate and the match rate, such as MAE, mean squared error (MSE), MAPE, and IoU. Among them, the mean absolute error can be checked most intuitively because there is no change in the result value of the difference image because the inter-pixel calculation is performed in the calculation process. In addition, IoU is used as a measure of the accuracy between the corresponding regions by dividing the area of the intersection region of the two regions to be compared by the value of the sum region. Therefore, it was used to confirm the agreement rate between regions.  
In the case of the FPS experiment, after loading the 3D model in the WebXR environment, the experiment was conducted to measure the rendering speed. In addition, to improve the consistency of the experiment, the speed at which the 3D model is rendered was measured during the difference image experiment.