Considering the characteristics of the problem, such as the type of features, and the demanded output, RF-based methodologies were primarily chosen as learning models for this problem. We started by studying the influence of data sampling for the performance of the model, and it was observed that less sampling generally leads to less errors on the prediction, but took to high computational time. Focusing on the pair-wise distance, the declining trend of errors stops when the sampling rate reaches to 65%. More sampling rates result in the errors oscillating in a the range of 0.1 mm. Although the difference errors between the distances in the range of 45% to 100% remains about 0.1 mm, the sampling rate with the minimum distance (1.048 mm) was chosen as the final sampling rate (65%). Same argument has been carried out to select the sampling rate of 75% when the OF is set to be Hausdorff distance. The minimum distance with respect to the Hausdorff OF is reported near to 4.022 mm.