A hardware-assisted scalable solution for interactive volume rendering of time-varying data

Abstract

We present a scalable volume rendering technique that exploits lossy compression and low-cost commodity hardware to permit highly interactive exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3-D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 42 millions voxels each time step) at interactive rates. By clustering multiple PCs together we demonstrate the data-size scalability of our method. The frame rates achieved make possible the interactive exploration of data in the temporal, spatial, and transfer function domains. A comprehensive evaluation of our method based on experimental studies using data sets (up to 134 millions voxels per time step) from turbulence flow simulations is also presented.

In situ ray tracing and computational steering for interactive blood flow simulation

“…However, the cost of the latter is not a problem if performed in GPU hardware [30]. Furthermore, out-of-core rendering solutions that take advantage of the use of multiple processors and parallel I/O further enhance interactivity [31].…”

Importance-Driven Time-Varying Data Visualization

“…Guthe and Straßer [4] applied wavelet and MPEG compression to time-varying data and achieved real-time decompression and interactive playback with hardware acceleration. Lum et al [9] presented a scalable technique for time-varying data visualization where the DCT encoding in conjunction with a hardware-assisted palette decoding scheme was employed for interactive data exploration. Woodring et al [18] proposed direct rendering of time-varying data using high dimensional slicing and projection techniques.…”

Ray-casting time-varying volume data sets with frame-to-frame coherence

“…Direct time-varying volume rendering can be subdivided into two main categories: one that treats time-varying data as a special case of an n-D model 14,15,16,17 , and the other, that treats separately the time dimension from the spatial dimensions 11,12,8,18,24,27 . In our biomedical applications, the number of frames of the animation is small in comparison to the number of samples of the datasets.…”