A 3D interactive scene construction method for interior design based on virtual reality

Abstract

The demand for data information in indoor scenes has increased. However, the indoor scene model construction is relatively complex. Meanwhile, there are many measurement and positional deviations in the current scene. Therefore, virtual reality technology and deep learning algorithms are used to build indoor scenes. The deep neural network and multi-point perspective imaging algorithm are used to analyze the image pixels of the scene, reduce the noise in current scene image recognition, and achieve the three-dimensional model construction of indoor scenes. The research results indicated that the new method improved the accuracy of indoor 3D scenes by eliminating noise in 3D scene data and constructing image data. The accuracy of the new method for item recognition was above 93%. Simultaneously, it can complete the construction of 3D scenes. The accuracy value of the new method was 3.00% higher than that of the CNN algorithm and 4.00% higher than that of the SVO algorithm. The error value was stable within the range of 0.2–0.3. At the same time, the loss function value of the algorithm used in this study was relatively small. The algorithm performance is more stable. From this, the new method model can accurately construct scenes, which has certain research value for indoor 3D scene construction.