We present a parallel compositing algorithm for Volumetric Depth Images (VDIs) of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. VDIs are view-dependent piecewise constant representations of volume data that offer a potential solution. They are more compact and less expensive to render than the original data. So far, however, there is no method for generating VDIs from distributed data. We propose an algorithm that enables this by sort-last parallel generation and compositing of VDIs with automatically chosen content-adaptive parameters. The resulting composited VDI can then be streamed for remote display, providing responsive visualization of large, distributed volume data.
%0 Journal Article
%1 Gupta2022-yx
%A Gupta, Aryaman
%A Incardona, Pietro
%A Brock, Anton
%A Reina, Guido
%A Frey, Steffen
%A Gumhold, Stefan
%A Günther, Ulrik
%A Sbalzarini, Ivo F
%D 2022
%I arXiv
%K topic_visualcomputing topic_lifescience
%T Parallel compositing of Volumetric Depth Images for interactive visualization of distributed volumes at high frame rates
%X We present a parallel compositing algorithm for Volumetric Depth Images (VDIs) of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. VDIs are view-dependent piecewise constant representations of volume data that offer a potential solution. They are more compact and less expensive to render than the original data. So far, however, there is no method for generating VDIs from distributed data. We propose an algorithm that enables this by sort-last parallel generation and compositing of VDIs with automatically chosen content-adaptive parameters. The resulting composited VDI can then be streamed for remote display, providing responsive visualization of large, distributed volume data.
@article{Gupta2022-yx,
abstract = {We present a parallel compositing algorithm for Volumetric Depth Images (VDIs) of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. VDIs are view-dependent piecewise constant representations of volume data that offer a potential solution. They are more compact and less expensive to render than the original data. So far, however, there is no method for generating VDIs from distributed data. We propose an algorithm that enables this by sort-last parallel generation and compositing of VDIs with automatically chosen content-adaptive parameters. The resulting composited VDI can then be streamed for remote display, providing responsive visualization of large, distributed volume data.},
added-at = {2024-09-10T10:41:24.000+0200},
author = {Gupta, Aryaman and Incardona, Pietro and Brock, Anton and Reina, Guido and Frey, Steffen and Gumhold, Stefan and G{\"u}nther, Ulrik and Sbalzarini, Ivo F},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/26b6300697759f9a5c6164ddb9bba42ba/scadsfct},
interhash = {3fc0741f7b21792be12ac73b96917852},
intrahash = {6b6300697759f9a5c6164ddb9bba42ba},
keywords = {topic_visualcomputing topic_lifescience},
publisher = {arXiv},
timestamp = {2024-11-22T15:50:05.000+0100},
title = {Parallel compositing of Volumetric Depth Images for interactive visualization of distributed volumes at high frame rates},
year = 2022
}
