The State of the Art in Flow Visualization: Dense and Texture‐Based Techniques

TLDR

Flow visualization has long attracted scientific research, and the growing size of multivariate datasets combined with advances in computing power have recently revived interest, particularly in texture‑based methods. This paper surveys dense, texture‑based flow visualization techniques. The authors describe how these techniques deliver a complete, densely sampled, spatio‑temporally coherent representation of flow fields, categorize related solutions, and discuss their fundamentals, strengths, and weaknesses.

Abstract

Abstract Flow visualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture‐based techniques. In this paper, dense, texture‐based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio‐temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods.