Abstract
Volumetric scalar data sets are common in many scientific, engineering and medical applications where they originate from measurements or simulations. Furthermore, they can represent geometric scene content, e.g. as distance or density fields. Often isosurfaces are extracted, either for indirect volume visualization in the former category, or to simply obtain a polygonal representation in case of the latter. However, even moderately sized volume data sets can result in complex isosurfaces which are challenging to recompute in real time, e.g. when the user modifies the isovalue or when the data itself are dynamic. In this paper, we present a GPU-friendly algorithm for the extraction of isosurfaces, which provides adaptive level of detail rendering with view-dependent tessellation. It is based on a longest edge bisection scheme where the resulting tetrahedral cells are subdivided into four hexahedra, which then form the domain for the subsequent isosurface extraction step. Our algorithm generates meshes with good triangle quality even for highly non-linear scalar data. In contrast to previous methods, it does not require any stitching between regions of different levels of detail. As all computation is performed at run time and no pre-processing is required, the algorithm naturally supports dynamic data and allows us to change isovalues at any time.
Volumetric scalar data sets are common in many scientific, engineering and medical applications where they originate from measurements or simulations. Furthermore, they can represent geometric scene content, e.g. as distance or density fields. Often isosurfaces are extracted, either for indirect volume visualization in the former category, or to simply obtain a polygonal representation in case of the latter. However, even moderately sized volume data sets can result in complex isosurfaces which are challenging to recompute in real time, e.g. when the user modifies the isovalue or when the data itself are dynamic.