IEEE VIS 2024 Content: Estimation and Visualization of Isosurface Uncertainty from Linear and High-Order Interpolation Methods

Estimation and Visualization of Isosurface Uncertainty from Linear and High-Order Interpolation Methods

Timbwaoga A. J. Ouermi - Scientific Computing and Imaging Institute, Salk Lake City, United States

Jixian Li - University of Utah, Salt Lake City, United States

Tushar M. Athawale - Oak Ridge National Laboratory, Oak Ridge, United States

Chris R. Johnson - University of Utah, Salt Lake City, United States

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Room: Bayshore VI

2024-10-14T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-14T12:30:00Z
Exemplar figure, described by caption below
Our proposed visualization system highlights errors introduced by linear interpolation methods and allows users to query local vertex differences between interpolation methods. The first column shows the approximated isosurface uncertainty and local selection using the colormap and transparent box, respectively. The second column shows the differences between linear and cubic, linear and WENO, and the approximated error for each vertex inside the transparent boxes. The third column shows a global comparison between linear and WENO. The fourth and fifth columns show a comparison between isosurfaces with (transparent orange) and without (opaque blue) possible hidden features that indicate isosurface feature uncertainty.
Abstract

Isosurface visualization is fundamental for exploring and analyzing 3D volumetric data. Marching cubes (MC) algorithms with linear interpolation are commonly used for isosurface extraction and visualization.Although linear interpolation is easy to implement, it has limitations when the underlying data is complex and high-order, which is the case for most real-world data. Linear interpolation can output vertices at the wrong location. Its inability to deal with sharp features and features smaller than grid cells can create holes and broken pieces in the extracted isosurface. Despite these limitations, isosurface visualizations typically do not include insight into the spatial location and the magnitude of these errors. We utilize high-order interpolation methods with MC algorithms and interactive visualization to highlight these uncertainties. Our visualization tool helps identify the regions of high interpolation errors. It also allows users to query local areas for details and compare the differences between isosurfaces from different interpolation methods. In addition, we employ high-order methods to identify and reconstruct possible features that linear methods cannot detect.We showcase how our visualization tool helps explore and understand the extracted isosurface errors through synthetic and real-world data.