Abstract: In recent years, the application of Virtual Reality (VR) technology in the field of interior environmental design has expanded significantly, offering designers innovative methods to present complex design concepts within virtual spaces. However, the current color matching and light and shadow processing in reality are not mature enough, and the deep learning algorithms applied in VR are relatively basic with low running efficiency. The consistency and authenticity of virtual reality are not stable enough. This paper explores the integration of color matching and light-shadow processing in interior environmental design within VR technology, with a particular emphasis on leveraging neural network models to achieve automated design optimization. By incorporating deep learning algorithms, this study proposes a neural network-based approach to enhance color matching and light-shadow processing, aiming to improve the realism and aesthetic appeal of virtual environments. Experimental results demonstrate that this method offers substantial advantages in terms of color matching accuracy, naturalness of light-shadow effects, and computational efficiency, highlighting its broad potential for application in virtual reality.

Ji Yang and Meifen Song, “Color Matching and Light and Shadow Processing in Intelligent Interior Environment Art Design Analysis and Application Based on Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151150

@article{Yang2024,
title = {Color Matching and Light and Shadow Processing in Intelligent Interior Environment Art Design Analysis and Application Based on Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151150},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151150},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Ji Yang and Meifen Song}
}