This is an archival copy of the Visualization Group's web page 1998 to 2017. For current information, please vist our group's new web page.
Flying Through the Known Universe
Problem Statement and Goals
Modern astronomy heavily relies on producing, analyzing and visualizing catalogs of objects (galaxies, quasars, supernovae, etc). The Sloan Digital Sky Survey has produced 3D maps containing more than 900K galaxies and 120K quasars. While the data is inherently 3D in nature, scientists continue to use 2D maps and 2D displays for visualization purposes. The goal of this project was to develop a proof-of-concept rendering pipeline that could process the SDSS Data Release DR10 dataset and produce a 3D stereoscopic flythrough.
Implementation and Results
We used the open source rendering tool Blender for importing the DR10 csv dataset. The dataset consists of galaxy positions (in spherical co-ordinates and redshift) and optical characteristics. We created 512 templates representing prominent classes of galaxies, and mapped all the Sloan data to these templates. Representing images as 2D postcards can create undesirable artefacts in a 3D flythrough. We used the Blender's billboard capability to produce a seamless flythrough effect. We also used Blender's python scripting and parallel rendering capabilities to generate individual frames for the movie in parallel.
Impact
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| Single frame of a longer movie showing the constellation Virgo. |
This collaboration produced the first 3D rendering of the SDSS DR10 dataset. Traditional ground based imagery allows physicists to examine datasets from a ``2D'' earth-centric perspective. By embedding the galaxy images in 3D, we were able to provide physicists with the capability to examine the data from multiple vantage points.
The movie was showcased in the 3D Film Festival in LA (September 2012) (More information at Today at Berkeley Lab.)
Left eye preview of the entire 3D movie at YouTube.
Collaborators
David Schlegel (Physics), Peter Nugent (CRD), Matt George (Physics), Prabhat (CRD), Yushu Yao (NERSC).