3D Reconstruction of Indoor Scenes Based on 3DGS Models

Abstract With the rapid development of computer vision and artificial intelligence technologies, indoor scene reconstruction has been more and more widely used in the fields of virtual reality, augmented reality and architectural design. In this paper, we study an indoor scene reconstruction method based on the 3DGS model, which has been widely used in computer graphics and vision processing with powerful scene representation and rendering capabilities. In this study, we optimize the 3DGS model to enhance the detail preservation and realism of the reconstruction results by adjusting the opacity of the Gaussian function. We used the Replica dataset and the self-harvested dataset for model training. Through experimental validation, the peak signal-to-noise ratio as well as the structural similarity ratio of the reconstruction results of the optimized model have an improvement effect of more than 1%, which indicates that the optimized model has a significant improvement in detail retention and realism, and the reconstructed scene performs more realistically in terms of texture details and light and shadow effects.