2 research outputs found
Fast Compressed Segmentation Volumes for Scientific Visualization
Voxel-based segmentation volumes often store a large number of labels and
voxels, and the resulting amount of data can make storage, transfer, and
interactive visualization difficult. We present a lossless compression
technique which addresses these challenges. It processes individual small
bricks of a segmentation volume and compactly encodes the labelled regions and
their boundaries by an iterative refinement scheme. The result for each brick
is a list of labels, and a sequence of operations to reconstruct the brick
which is further compressed using rANS-entropy coding. As the relative
frequencies of operations are very similar across bricks, the entropy coding
can use global frequency tables for an entire data set which enables efficient
and effective parallel (de)compression. Our technique achieves high throughput
(up to gigabytes per second both for compression and decompression) and strong
compression ratios of about 1% to 3% of the original data set size while being
applicable to GPU-based rendering. We evaluate our method for various data sets
from different fields and demonstrate GPU-based volume visualization with
on-the-fly decompression, level-of-detail rendering (with optional on-demand
streaming of detail coefficients to the GPU), and a caching strategy for
decompressed bricks for further performance improvement.Comment: IEEE Vis 202
Stochastic Volume Rendering of Multi-Phase SPH Data
In this paper, we present a novel method for the direct volume rendering of large smoothed‐particle hydrodynamics (SPH) simulation data without transforming the unstructured data to an intermediate representation. By directly visualizing the unstructured particle data, we avoid long preprocessing times and large storage requirements. This enables the visualization of large, time‐dependent, and multivariate data both as a post‐process and in situ. To address the computational complexity, we introduce stochastic volume rendering that considers only a subset of particles at each step during ray marching. The sample probabilities for selecting this subset at each step are thereby determined both in a view‐dependent manner and based on the spatial complexity of the data. Our stochastic volume rendering enables us to scale continuously from a fast, interactive preview to a more accurate volume rendering at higher cost. Lastly, we discuss the visualization of free‐surface and multi‐phase flows by including a multi‐material model with volumetric and surface shading into the stochastic volume rendering