Completely Adaptive Simplification of Massive Meshes

The growing availability of massive models and the inability of most existing visualization tools to work with them requires efficient new methods for massive mesh simplification. In this paper, we present a completely adaptive, virtual memory based simplification algorithm for large polygonal datasets. The algorithm is an enhancement of RSimp [2], enabling out of core simplification without reducing the output quality of the original algorithm. The primary improvement in our new algorithm, VMRSimp, builds and preserves memory locality throughout the simplification process. This is crucial for successful simplification of massive meshes. An additional enhancement based on a depthfirst simplification approach improves running time further by increasing reuse of the resident working set, at the cost of a minor reduction in output quality. VMRSimp performs completely adaptive simplification of massive meshes at reasonable rates, reducing for example 18 million to 100 thousand vertices in 51 minutes. This permits simplification to output sizes in the millions without thrashing, improves accuracy for all smaller output sizes, and enables sensitivity in simplification to mesh boundaries and topological changes. CR