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 point-based 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 point-based 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.