4.11 Rapid visuomotor processes drive the leg regardless of balance constraints

Abstract

There is still ample room for further optimization of the application effect of virtual reality (VR) in smart products, and further expansion of the reconstruction research of smart home products. Hence, the related algorithms of spatial reconstruction and three-dimensional (3D) Computer-Aided Design (CAD) item reconstruction is proposed based on the relevant concepts of CAD and convolutional neural network. The algorithms aim to construct the virtual application scene and the simulation state of product usage by VR technology, and stimulate customers’ purchase decision. Through the experiments, it is found that the Fully Convolutional Network optimized by Visual Geometry Group Network can effectively realize scene reconstruction and item reconstruction. The average overall recognition accuracy of scene segmentation is 91.3%, and the mean Intersection over Union is 63.4%, showing a better recognition effect than other similar algorithms. Moreover, in the detection of indoor items, the maximum detection accuracy of the MaskR-CNN algorithm is 0.89, and the detection effect is also better than that of other algorithms. The experiments achieve a good spatial reconstruction structure, with the average voxel Prec rates in unsheltered space and sheltered space of 94.4% and 55.6%, and the average voxel Recall of 92.2% and 36.5%, respectively. The experimental results indicate an excellent spatial reconstruction effect. This study can provide a more scientific reference for subsequent VR system research.