PLIC: Bridging the Gap Between Streamlines and LIC

Abstract:

This paper lays the groundwork for comparing flow visualizations using streamlines and line integral convolution (LIC). Our approach is to identify and define relevant parameters in each of these flow visualization techniques. Mapping strategies are then designed to generate LIC-like images from streamlines and streamline-like images from LIC. The result is a technique which we call pseudo-LIC or PLIC. The main contribution being reported in this paper is a methodology for flexibly generating flow visualizations that span the spectrum of streamline-like to LIC-like. Among the advantages are: performance speedups over LIC, applicability to time varying data sets, and variable-speed animation.

Paper:

A pdf version of the paper is available. A separate color plate is available here.

Images:

These images were generated with our PLIC method.

Movies:

The movies present the result of a new flow visualization technique called pseudo-Line Integral Convolution or PLIC. PLIC combines the advantages of traditional streamlines and LIC. PLIC can generate images that look like LIC but are faster to compute. PLIC is flexible enough to generate flow visualizations that span the spectrum of streamline-like to LIC-like. Some other advantages of PLIC are applicability to time-varying data and variable speed animation.

The audio was removed from these movies so that they are not huge and easy to download. The movies are in the quick-time format and were compressed using gzip.
Please use the loop mode of your movieplayer to watch these movies.

All animations are pseudo-colored with the velocity magnitude.

Animation of steady flow:

Unsteady flow for the Dynamic Vortices dataset:
comparison of PLIC with Enhanced-LIC and UFLIC. Unsteady flow for the Dynamic Vortices dataset:
Closeups of Enhanced-LIC, UFLIC, and PLIC. Unsteady flow for the Delta wing dataset:
Closeups of Enhanced-LIC, UFLIC, and PLIC.

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