Accelerated Isosurface Extraction Approaches

Abstract

The marching cubes [7,15] method demonstrated that isosurface extraction can be reduced, using a divide-and-conquer approach, to solving a local triangulation problem. In addition, the marching cubes method proposed a simple and efficient local triangulation using a lookup table. However, the marching cubes did not address the divide portion of the approach, i.e., how to efficiently search a large dataset for these small local triangulations. To this end, the marching cubes method checks each and every cell of the dataset.In this chapter, we introduce the three main approaches to accelerate isosurface extraction, (Section 2.2) and present two specific methods. Section 2.3 introduces the Span Space metaphor and uses it to devise a near-optimal search method in Section 2.4. Finally, Section 2.5 examines the view-dependent extraction approach and presents a particular implementation.

Multivariate Probabilistic Range Queries for Scalable Interactive 3D Visualization

“…Span space [34,35] methods can be used to optimize isosurface extraction from scalar volumes, or to quickly search for cells that fall within a given min-max range. Near optimal isosurface extraction (NOISE) [35] uses kd-trees [6], and isosurfacing in span space with utmost efficiency (ISSUE) [51] uses 2D regular lattices for fast span space search.…”

Visualization of Cosmological Particle-Based Datasets

“…Most isosurface extraction techniques operate only on a grid, whether structured or unstructured [27]. Our particle simulation produces a point cloud with no explicit order or connectivity information, which complicates using an unstructured grid representation for isosurface extraction.…”

“…Most isosurface extraction methods operate only on structured data usually a structured or unstructured grid [27]. Livnat [27] and Sutton et al [39] provide overviews of popular isosurface extraction techniques.…”