Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY

RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization

Abstract: In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. We devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

Teaser 1: The below shows the RadVolViz system interface with all major displays and functionalities, here using a Chemically Sensitive Electron Tomography Dataset:

In this figure, (C) is the rendered volume dataset; (B) is the interactive multivariate information display which doubles as the volume transfer function editor (MTE for short); (A) is used for manual coloring within the MTE; (D) controls the MTE’s lens operations; (E)-(G) are various control elements to tune rendering parameters; (H) is the interface for volume clipping; (I) is a frames/s rendering performance gauge.

Teaser 2: Next we show various renderings from the Hurricane Isabel dataset we obtined with our system:

During setup, we noticed that of the 13 variables, only 4 (temperature, speed, pressure, and vapor) had significant variation and so we focused on these 4 to make the best use of the MTE’s real estate. (g-h) MTE and generic rendering with the min/max value ranges indicated by the two slider settings on top of the image. Even from this “out of the box” rendering it is already apparent that the eye is dominated by temperature (light blue to green color) while the eye’s periphery adds yellow to the green indicating a growing dominance of speed. Indeed, the wall around the eye is typically the storm’s strongest part and it pulls in warmer ocean water that increases the temperature in the eye. As the water is pulled up, moving further away from the eye the vapor is increasing (blue-tone colors) and finally the pressure (red-tone color). (a-f) Lens-driven renderings where we more clearly observe the high temperature in the eye (a-b), a classic cloud rendering of the vapor in grey-blue (c-d), and the pressure-dominated outer periphery in red-orange (e-f). (j-l) Colorizations using the manual brushings shown in (i) with different min-value slider settings; the regions with speed-dominated data are colored into blue. (k) has the higher min-value which only allows the higher speed values to render, indicating the high speed near the storm’s eye. (l) rotated visualization allowing a clearer view into the storm’s lower layers.

Video: Watch it to get a quick overview:

Paper:A. Kumar, X. Zhang, H. Xin, H. Yan, X. Huang, W. Xu, K. Mueller, "RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization," IEEE Trans. on Visualization and Computer Graphics, (to appear) 2023 PDF PPT

Funding: This research was partially supported by NSF grants IIS 1527200 and 1941613 and by Brookhaven National Laboratory LDRD grant 16-041. Huolin L. Xin was supported by NSF Award CHE-1900401. This research used resources of the hard x-ray nanoprobe beamline (HXN) at 3ID of the National Synchrotron Light Source II, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No. DE-SC0012704.