Level of detail (LOD) is a technique where geometric objects are represented at a number of resolutions, allowing the workload of the system to be modulated on-line. There are numerous schemes for implementing LOD, using selection criteria based upon an object's distance, size, velocity, or eccentricity. However, little is known about how to specify optimally when a particular LOD should be selected so that the user is not aware of any visual change, or to what extent any particular LOD scheme can improve an application's performance. In response, this paper produces a generic, orthogonal model for LOD based upon data from the field of human visual perception. The effect of this model on the system is evaluated to discover the contribution that each component makes towards any performance improvement.The results suggest that both velocity and eccentricity LOD should be implemented together (if at all) because their individual contribution is likely to be negligible. Also, it is apparent that size (or distance) optimisations offer the greatest benefit, contributing around 95% of any performance increment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.