Marching tetrahedra

About: Marching tetrahedra is a research topic. Over the lifetime, 226 publications have been published within this topic receiving 20530 citations.

Marching cubes: A high resolution 3D surface construction algorithm

Abstract: We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.

Quantitative analysis of molecular surface based on improved Marching Tetrahedra algorithm.

Abstract: Quantitative analysis of molecular surface is a valuable technique for analyzing non-covalent interaction, studying molecular recognition mode, predicting reactive site and reactivity. An efficient way to realize the analysis was first proposed by Bulat et al. (J. Mol. Model., 16, 1679), in which Marching Tetrahedra (MT) approach commonly used in computer graphics is employed to generate vertices on molecular surface. However, it has been found that the computations of the electrostatic potential in the MT vertices are very expensive and some artificial surface extremes will be presented due to the uneven distribution of MT vertices. In this article, we propose a simple and reliable method to eliminate these unreasonably distributed surface vertices generated in the original MT. This treatment can save more than 60% of total analysis time of electrostatic potential, yet the loss in accuracy is almost negligible. The artificial surface extremes are also largely avoided as a byproduct of this algorithm. In addition, the bisection iteration procedure has been exploited to improve accuracy of linear interpolation in MT. The most appropriate grid spacing for surface analysis has also been investigated. 0.25 and 0.20 bohr are recommended to be used for surface analysis of electrostatic potential and average local ionization energy, respectively.

Octrees for faster isosurface generation

Abstract: The large size of many volume data sets often prevents visualization algorithms from providing interactive rendering. The use of hierarchical data structures can ameliorate this problem by storing summary information to prevent useless exploration of regions of little or no current interest within the volume. This paper discusses research into the use of the octree hierarchical data structure when the regions of current interest can vary during the application, and are not known a priori. Octrees are well suited to the six-sided cell structure of many volumes.A new space-efficient design is introduced for octree representations of volumes whose resolutions are not conveniently a power of two; octrees following this design are called branch-on-need octrees (BONOs). Also, a caching method is described that essentially passes information between octree neighbors whose visitation times may be quite different, then discards it when its useful life is over.Using the application of octrees to isosurface generation as a focus, space and time comparisons for octree-based versus more traditional “marching” methods are presented.

Feature sensitive surface extraction from volume data

Abstract: The representation of geometric objects based on volumetric data structures has advantages in many geometry processing applications that require, e.g., fast surface interrogation or boolean operations such as intersection and union. However, surface based algorithms like shape optimization (fairing) or freeform modeling often need a topological manifold representation where neighborhood information within the surface is explicitly available. Consequently, it is necessary to find effective conversion algorithms to generate explicit surface descriptions for the geometry which is implicitly defined by a volumetric data set. Since volume data is usually sampled on a regular grid with a given step width, we often observe severe alias artifacts at sharp features on the extracted surfaces. In this paper we present a new technique for surface extraction that performs feature sensitive sampling and thus reduces these alias effects while keeping the simple algorithmic structure of the standard Marching Cubes algorithm. We demonstrate the effectiveness of the new technique with a number of application examples ranging from CSG modeling and simulation to surface reconstruction and remeshing of polygonal models.