Out-of-core build of a topological data structure from polygon soup

Abstract

Many solid modeling applications require information not only about the geometry of an object but also about its topology. Most interchange formats do not provide this information, which the application must then derive as it builds its own topological data structure from unordered, “polygon soup” input. For very large data sets, the topological data structure itself can be bigger than core memory, so that a naive algorithm for building it that doesn't take virtual memory access patterns into account can become prohibitively slow due to thrashing. In this paper, we describe a new out-of-core algorithm that can build a topological data structure efficiently from very large data sets, improving performance by two orders of magnitude over a naive approach.