A low-complexity virtual reality modeling method based on ontology grid sparsity

摘要

This paper focuses on an innovative low-complexity modeling method for virtual reality. Voxels are the basic unit of pixels in 3D (three dimensions), and when stored and processed in a computer, they contain more data redundancy than the demand for actual human vision presentations. This excess is known as voxel redundancy. The reason for the redundancy is that infinite-dimensional expansion and heterogeneous voxel features have merged in modeling methods. The major voxel compressions and human factors correlations are important to consider. Therefore, we propose a novel low-complexity virtual reality based on ontology grid sparsity. The redundancy performance of the existing virtual reality modeling methods is first analyzed. The high-dimension modeling method is designed to describe the base elements that are needed for humans to distinguish what is actually perceived. Based on the ontology theory, grid sparsity is exploited to construct the correlation model. We set up the relevant degree and sensitivity level calculations to classify the correlations between ontology features and other features. The features with weak sensitivity are ignored or merged, which successfully reduces the dimensions and redundancy that are needed to represent virtual items. Visual feature integration combines human factors into a voxel model. Finally, we build the virtual laboratory as a test case in the Unity 3D and 3DMax environment. The tests demonstrate that the new modeling method offers 21.2% and 34.7% of presentation delay and redundancy improvement, respectively.