Abstract
GPU-assisted multi-eld rendering provides a means of generating effective video volume visualization that can convey both the objects in a spatiotemporal domain as well as the motion status of these objects. In this paper, we present a technical framework that enables combined volume and ow visualization of a video to be synthesized using GPU-based techniques. A bricking-based volume rendering method is deployed for handling large video datasets in a scalable manner, which is particularly useful for synthesizing a dynamic visualization of a video stream. We have implemented a number of image processing lter s, and in particular, we employ an optical ow lter for estimating motion ows in a video. We have devised mechanisms for combining volume objects in a scalar eld with glyph and streamline geometry from an optical ow . We demonstrate the effectiveness of our approach with example visualizations constructed from two benchmarking problems in computer vision.
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