Calculating visual perception of entities in simulations requires complex intersection tests between the line of sight and the virtual world. In this study, we focus on outdoor environments which consist of a terrain and various objects located on terrain. Using hardware capabilities of graphics cards, such as occlusion queries, provides a fast method for implementing these tests. In this thesis, we introduce an approach for better load balancing of visibility detection and target acquisition calculations by the use of occlusion queries. Our results show that, the proposed approach is 1.5 to 2 times more efficient than the existing algorithms on the average.

The purpose of this thesis is to present methods for visibility determination and to design and implement an application to demonstrate visibility determination in a 3D maze.

In large scale entity-level military force-on-force simulations it is essential to know when one entity can visibly see another entity. This visibility determination plays an important role in the simulation and can affect the outcome of the simulation. When virtual Computer Generated Forces (CGF) are introduced into the simulation these intervisibilities must now be calculated by the virtual entities on the battlefield. But as the simulation size increases so does the complexity of calculating visibility between entities. This thesis presents an algorithm for performing these visibility calculations using Graphical Processing Units (GPU) instead of the Central Processing Units (CPU) that have been traditionally used in CGF simulations. This algorithm can be distributed across multiple GPUs in a cluster and its scalability exceeds that of CGF-based algorithms. The poor correlations of the two visibility algorithms are demonstrated showing that the GPU algorithm provides a necessary condition for a "Fair Fight" when paired with visual simulations.