Level-of-Detail Rendering of Large-Scale Irregular Volume Datasets Using Particles

Abstract

This paper describes a level-of-detail rendering technique for large-scale irregular volume datasets. It is well known that the memory bandwidth consumed by visibility sorting becomes the limiting factor when carrying out volume rendering of such datasets. To develop a sorting-free volume rendering technique, we previously proposed a particle-based technique that generates opaque and emissive particles using a density function constant within an irregular volume cell and projects the particles onto an image plane with sub-pixels. When the density function changes significantly in an irregular volume cell, the cell boundary may become prominent, which can cause blocky noise. When the number of the sub-pixels increases, the required frame buffer tends to be large. To solve this problem, this work proposes a new particle-based volume rendering which generates particles using metropolis sampling and renders the particles using the ensemble average. To confirm the effectiveness of this method, we applied our proposed technique to several irregular volume datasets, with the result that the ensemble average outperforms the sub-pixel average in computational complexity and memory usage. In addition, the ensemble average technique allowed us to implement a level of detail in the interactive rendering of a 71-million-cell hexahedral volume dataset and a 26-million-cell quadratic tetrahedral volume dataset.

Fused Visualization for Large-Scale Time-Varying Volume Data with Adaptive Particle-Based Rendering

“…Object-space particle-based rendering technique is a rendering technique based opaque particles in the object space. The original version is proposed by Koyamada et al [1] [10] [11] [12]. In this technique, a set of opaque particles is generated from a given 3D scalar field based on a user-specified transfer function.…”

High-quality particle-based volume rendering for large-scale unstructured volume datasets

“…In this technique, the required frame buffer size becomes large depending on the sub-pixel level when performing sub-pixel processing. To solve this problem, the ensemble average process was developed Kawamura et al 2010). Repetition processing required for the ensemble averaging can reduce the memory cost but requires more rendering time than sub-pixel processing.…”

“…Metropolis sampling for PBVR (Kawamura et al 2010) first calculates the particle density values at each cell vertex and then interpolates for arbitrary positions in the volume cell. The density values are smoothed even if they change drastically in the volume cell, which results in a lowquality image.…”