Virtual Reality Panoramic Image Generation System Based on Machine Learning Algorithm

Abstract

With the increase in computational power and math power, machine learning has made great progress recently, and its application in the field of computer vision is also well known. More and more people apply deep neural network in image feature extraction, image classification, fake data identification, and image generation. The purpose of this paper is to simulate the virtual reality panoramic image generation system based on machine learning algorithm. In order to propose a reliable and interpretable machine learning image generation model, and to avoid the mode collapse and mode confusion problems in the generated model, this paper will focus on the most the optimal transmission theory is applied to the image generation model. The use of Monte Carlo methods to generalize semi-continuous machine learning algorithms to arbitrary dimensions makes it possible to combine them with deep learning models. The results of the comparison between the running speed and the convergence accuracy of the machine algorithm show that when the number of sampling points is the same in the low-dimensional space, the optimal transmission has the same convergence accuracy on the CPU and GPU, and the computing efficiency on the GPU is about 3 times that of the CPU computing efficiency. In high-dimensional space, computing on GPU also improves the computational efficiency of machine learning algorithms.