Simulation of Indoor Scene 3D Reconstruction Based on High and Low Frequency Feature Enhancement Algorithm
In order to improve the efficiency and quality of interior design,a 3D reconstruction simulation method for indoor scenes based on high and low frequency feature enhancement algorithm is proposed.Combined with long short-term memory networks to estimate single image plane parameters of indoor scenes,the paper extracts high and low frequency features of a single indoor scene image using wavelet transform algorithm,enhances the high and low frequency feature images through weighted guided filtering algorithm and uses deep neural networks to predict segmentation and depth maps of images.Afterwards,integrate the segmentation map,depth map,and plane parameters to achieve 3D reconstruction.The experimental results show that this method can effectively extract high and low frequency features of indoor scene single images,enhance high and low frequency feature images,and improve image clarity.The highest mean square normalization error of 3D reconstruction is 0.15.
high and low frequency feature enhancement algorithmindoor scenessingle imagesegmented planethree-dimensional reconstruction