Fourier Opacity Optimization for Scalable Exploration
Rojo IB, Gross M, Günther T (2020)
Publication Type: Journal article
Publication year: 2020
Journal
Book Volume: 26
Pages Range: 3204-3216
Article Number: 8713907
Journal Issue: 11
DOI: 10.1109/TVCG.2019.2915222
Abstract
Over the past decades, scientific visualization became a fundamental aspect of modern scientific data analysis. Across all data-intensive research fields, ranging from structural biology to cosmology, data sizes increase rapidly. Dealing with the growing large-scale data is one of the top research challenges of this century. For the visual exploratory data analysis, interactivity, a view-dependent visibility optimization and frame coherence are indispensable. In this work, we extend the recent decoupled opacity optimization framework to enable a navigation without occlusion of important features through large geometric data. By expressing the accumulation of importance and optical depth in Fourier basis, the computation, evaluation and rendering of optimized transparent geometry become not only order-independent, but also operate within a fixed memory bound. We study the quality of our Fourier approximation in terms of accuracy, memory requirements and efficiency for both the opacity computation, as well as the order-independent compositing. We apply the method to different point, line and surface data sets originating from various research fields, including meteorology, health science, astrophysics and organic chemistry.
Authors with CRIS profile
Involved external institutions
Switzerland (CH)How to cite
APA:
Rojo, I.B., Gross, M., & Günther, T. (2020). Fourier Opacity Optimization for Scalable Exploration. IEEE Transactions on Visualization and Computer Graphics, 26(11), 3204-3216. https://dx.doi.org/10.1109/TVCG.2019.2915222
MLA:
Rojo, Irene Baeza, Markus Gross, and Tobias Günther. "Fourier Opacity Optimization for Scalable Exploration." IEEE Transactions on Visualization and Computer Graphics 26.11 (2020): 3204-3216.
BibTeX: Download