High‐speed Marching Cubes using HistoPyramids

Abstract

We present an implementation approach for Marching Cubes (MC) on graphics hardware for OpenGL 2.0 or comparable graphics APIs. It currently outperforms all other known graphics processing units (GPU)-based iso-surface extraction algorithms in direct rendering for sparse or large volumes, even those using the recently introduced geometry shader (GS) capabilites. To achieve this, we outfit the Histogram Pyramid (HP) algorithm, previously only used in GPU data compaction, with the capability for arbitrary data expansion. After reformulation of MC as a data compaction and expansion process, the HP algorithm becomes the core of a highly efficient and interactive MC implementation. For graphics hardware lacking GSs, such as mobile GPUs, the concept of HP data expansion is easily generalized, opening new application domains in mobile visual computing. Further, to serve recent developments, we present how the HP can be implemented in the parallel programming language CUDA (compute unified device architecture), by using a novel 1D chunk/layer construction

An Adaptive Model for Particle Fluid Surface Reconstruction

“…We have enforced the parallel method to the scalar field computation of [10] and our method. Additionally, the performance is improved by using the optimized MC method [4] and neighborhood searching [8]. Table 1 shows the data of the presented scenes for scalar field computation, including the solver, the ratio of the number of particles, the resolution of MC grids.…”

“…Recently, the high quality visualization of particle fluid surface is discussed in many literatures. Commonly employed methods include metaballs, point splatting, raycasting and marching cubes (MC) [1]- [4]. Premoze et al [5] introduced a notation of level set method to reconstruct surface from particles, however, breaking waves and droplets cannot be seen.…”

An Integrated System and Framework for Development of Medical Applications and Products Based on Medical Imaging Data

“…Marching Cube (MC) is an efficient method for extracting isosurfaces from 3D scalar field [6]. The MC algorithm is used in this study to reconstruct 3D models of a defective skull.…”

Cost-Effective Solutions and Tools for Medical Image Processing and Design of Personalised Cranioplasty Implants

“…The Hounsfield values range from -500 (e.g., lung) to +800 (e.g., bone). After conducting the image analysis and segmentation based on the grayscale values, 3D models of AoI are reconstructed from the serial CT images, based on the Marching Cubes algorithm [6,7]. The algorithm is implemented as follows.…”