With the rapid development of science and technology, virtual reality (VR) systems have developed rapidly. VR systems can be applied in many fields such as education, medical, military, and probing. According to the survey, the VR system will have a delay in the process of acquisition, transmission and processing, and more than 40% of the human body will have motion sickness after experiencing VR. The cause of motion sickness is delay. Accurate measurement of delays therefore helps to resolve motion sickness in the virtual world and provides guidance for the upgrade of these systems. In response to the existing problems, our research team designed a low-cost, high-performance experimental equipment that uses a single-chip microcomputer and a PIN photodiode as the hardware core of the delay detection device. The light intensity changes are used to determine the start and end of the detection. The algorithm calculates the delay time of the VR device.
With the powerful learning ability the neural network, fringe pattern can be effectively analyzed after calculating the phase of the fringe pattern. This paper proposes the combination algorithm of phase-shifting (PS) algorithm and structured light convolutional neural network (SL-CNN) that apply deep learning to Structural Light 3D reconstruction.