Neophytos Neophytou - 4D Splatting
Space Time Points: 4D Splatting on efficient grids
Neophytos Neophytou and Klaus Mueller
Abstract:
4D datasets, such as time-varying datasets, usually come on 4D Cartesian Cubic (CC) grids. In this paper, we explore the use of 4D Body Centered Cubic (BCC) grids to provide a more efficient sampling lattice. We use this lattice in conjunction with a pointbased renderer that further reduces the data into an RLE-encoded list of relevant points. We achieve compression ranging from 50% to 80% in our experiments. Our 4D visualization approach follows the hyperslice paradigm: the user first specifies a 4D slice to extract a 3D volume, which is then viewed using a regular pointbased full volume renderer. The slicing of a 4D BCC volume yields a 3D BCC volume, which theoretically has 70% of the datapoints of an equivalent CC volume. We reach compressions close to this in practice. The visual quality of the rendered BCC volume is virtually identical with that obtained from the equivalent CC volume, at 70-80% of the CC grid rendering time. Finally, we also describe a 3.5D visualization approach that uses motion blur to indicate the transition of objects along the dimension orthogonal to the extracted hyperslice in one still image. Our approach uses interleaved rendering of a motion volume and the current iso-surface volume to add the motion blurring effect with proper occlusion and depth relationships.
Paper:
N. Neophytou and K. Mueller, Space-time points: Splatting in 4D, Symposium on Volume Visualization and Graphics 2002, Boston, October 2002
Presentation:
PDF converted from presentation at Stony Brook GRC. Slightly updated from the version that was presented at VolVis '02.
Movies:
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Implementation Comments:
This project marks the first results of the Stony Brook splatter software. We have used the image aligned post-shaded splatting algorithm and adjusted it to render 3D and 4D BCC grids. We have used a novel transfer function editor with a carefully fine-tuned splatter to get the following images.
One would notice the BCC datasets to be slightly blurry. That is because they have been resampled from the original cartesian counterparts, and not reconstructed directly from the data source. It would also help if we used a more carefully fine-tuned resampling filter to produce the BCC sets.
Visible Human Foot: Cartesian and BCC
Engine Block isosurace: Cartesian and BCC
4D Vortex: Cartesian and BCC
4D Turbulent Jet:Cartesian and BCC
4D Jet Shockwave:Cartesian and BCC
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