Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

Abstract

In the past two decades, there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification. The major research areas of this field include object detection and object recognition. Moreover, wireless communication technologies are presently adopted and they have impacted the way of education that has been changed. There are different phases of changes in the traditional system. Perception of three-dimensional (3D) from two-dimensional (2D) image is one of the demanding tasks. Because human can easily perceive but making 3D using software will take time manually. Firstly, the blackboard has been replaced by projectors and other digital screens so such that people can understand the concept better through visualization. Secondly, the computer labs in schools are now more common than ever. Thirdly, online classes have become a reality. However, transferring to online education or e-learning is not without challenges. Therefore, we propose a method for improving the efficiency of e-learning. Our proposed system consists of two-and-a-half dimensional (2.5D) features extraction using machine learning and image processing. Then, these features are utilized to generate 3D mesh using ellipsoidal deformation method. After that, 3D bounding box estimation is applied. Our results show that there is a need to move to 3D virtual reality (VR) with haptic sensors in the field of e-learning for a better understanding of real-world objects. Thus, people will have more information as compared to the traditional or simple online education tools. We compare our result with the ShapeNet dataset to check the accuracy of our proposed method. Our proposed system achieved an accuracy of 90.77% on plane class, 85.72% on chair class, and car class have 72.14%. Mean accuracy of our method is 70.89%.