Near optimal hierarchical culling: performance driven use of hardware occlusion queries

Abstract

The most efficient general occlusion culling techniques are based on hardware accelerated occlusion queries. Although in many cases these techniques can considerably improve performance, they may still reduce efficiency compared to simple view frustum culling, especially in the case of low depth complexity. This prevented the broad use of occlusion culling in most commercial applications. In this paper we present a new conservative method to solve this problem, where the main idea is to use a statistical model describing the occlusion probability for each occlusion query in order to reduce the number of wasted queries which are the reason for the reduction in rendering speed. We also describe an abstract parameterized model for the graphics hardware performance. The parameters are easily measurable at startup and thus the model can be adapted to the graphics hardware in use. Combining this model with the estimated occlusion probability our method is able to achieve a near optimal scheduling of the occlusion queries. The implementation of the algorithm is straightforward and it can be easily integrated in existing real-time rendering packages based on common hierarchical data structures.