Coherent Hierarchical Level-of-Detail (HLOD) Refinement Through Hardware Occlusion Queries

We present a coherent hierarchical level of detail (HLOD) culling algorithm that employs a novel metric to perform the refinement of a HLOD-based system that takes into account visibility information. The information is gathered from the result of a hardware occlusion query (HOQ) performed on the bounding volume of a given node in the hierarchy. Although the advantages of doing this are clear, previous approaches treat refinement criteria and HOQ as independent subjects. For this reason, HOQs have been used restrictively as if their result were boolean. In contrast to that, we fully exploit the results of the queries to be able to take into account visibility information within refinement conditions. We do this by interpreting the result of a given HOQ as the virtual resolution of a screen space where the refinement decision takes place. In order to be able to use our proposed metric to perform the refinement of the HLOD hierarchy as well as to schedule HOQs, we exploit the spatial and temporal coherence inherent to hierarchical representations. Despite the simplicity of our approach, in our experiments we obtained a substantial performance boost (compared to previous approaches) in the frame-rate with minimal loss in image