Generative Hypergraph-based Kinematic Models for Virtual-Reality Applications
Publication Name: 2022 1st IEEE International Conference on Cognitive Aspects of Virtual Reality Cvr 2022
Publication Date: 2022-01-01
Volume: Unknown
Issue: Unknown
Page Range: 11-16
Description:
Recently, virtual reality applications have become a prominent research area, along with related topics such as digital-twin applications and simulation of devices such as vehicles or cyber-physical systems. These topics are intimately linked with computer-based simulations and computer graphics. Consequently, accurately describing entities populating simulated worlds is a critical task. Relevant properties include but are not limited to an object's visual appearance and kinematic constraints. This paper proposes a hypergraph-based kinematic model aiming to describe virtual entities used in virtual reality applications and simulations. Compared to other popular schematics (e.g., URDF, SDF), a primary advantage of this approach is its reduced language element set, which is capable of minimally describing a conceptually very simple kind of hypergraph. This reduction, in turn, enables the description of simple graph triplets amenable to storage in graph databases or ontologies. The introduced format is aimed at enabling the flexible and efficient real-time exchange of visual and physical information on cognitive channels between entities. The possibility of transforming an instance of this model into other schematics (SDF, MaxWhere) is further demonstrated in the paper, which constructively proves the expressive capabilities of the model and helps support the claim that it is equivalent in expressive power to other widely used description models.
Open Access: Yes