A Single-Pass Approach to Adaptive Simplification of Out-Of-Core Models

The algorithm efficiency of existing adaptable out-of-core simplification algorithms is relatively low because these methods often require scanning the original model over one time. In this paper, we present an adaptive clustering method, known as the Balanced Tiling (BT) method, for out-of-core simplification which only needs one pass over the input model. The key idea behind BT is that the model surface can be recorded in a memory efficient manner using surface coding and the global distribution of surface details can be obtained through quadric quantizing of the original model. Besides the feature edge areas, our algorithm can position the corner areas, which are not noticed by some other out-of-core simplification approaches such as the Fei's BR algorithm, by analyzing the Garland's quadric error matrix. Based on this analysis, adaptive clustering is achieved by restoring detail areas with cluster split operations while further simplifying smooth areas with edge collapse operations. BT is especially suitable for handling super huge meshes because the I/O time is greatly reduced. BT produces high quality approximations and the memory requirement is small, being only related with the size of the output model. © 2003 World Scientific Publishing Company.