Enhancing image style transfer for real-time indoor geometric data using GAN

Abstract

The need for two-dimensional (2D) simulated environments, such as those seen in VR/AR and the multiverse, has grown substantially in the rapidly emerging modern era. To reduce the need for human interaction, the present studies have focused on the automated transfer of image processing styles inside a 2D virtual world through the application of computer vision. However, there are several limits to the current research on 2D environment-style transfers that rely on modelling. One thing to keep in mind is that there is a significant amount of style image data required for training a style transfer network specifically for 2D simulations. All this information must be combined with perspectives that are fairly accurate representations of the internet. The second issue was that the 2D structures were inconsistent. Most of the research relies on 2D input image attributes and ignores 2D scene geometric data. Lastly, changing the way something looks does not change the fact that every object has its own distinct qualities. To address these issues, we propose an enhanced Generative Adversarial Networks (GAN) approach for image style transfer in which oversampling and fine tuning is used to improve the data loss. Image Style transfer is an approach to image-to-image translation that supports the transfer of the style of an image to a real-time image and is mainly used to enhance the resolution and quality of an image. The performance of the proposed approach is analyzed using the MIT dataset, which is retrieved from Kaggle. It is an open-source dataset that contains 67 indoor categories and has more than 15,000 images. The proposed approach is implemented using MATLAB simulation tool. The results of proposed approach show that, content loss and style loss are lower than other deep learning models such as VGG16 and Alex Net.