Screen Space Particle Selection
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Date
2018
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Analyses of large 3D particle datasets typically involve many different exploration and visualization steps. Interactive exploration techniques are essential to reveal and select interesting subsets like clusters or other sophisticated structures. State-of-the-art techniques allow for context-aware selections that can be refined dynamically. However, these techniques require large amounts of memory and have high computational complexity which heavily limits their applicability to large datasets. We propose a novel, massively parallel particle selection method that is easy to implement and has a processing complexity of O(n*k) (where n is the number of particles and k the maximum number of neighbors per particle) and requires only O(n) memory. Furthermore, our algorithm is designed for GPUs and performs a selection step in several milliseconds while still being able to achieve high-quality results.
Description
@inproceedings{10.2312:cgvc.20181208,
booktitle = {Computer Graphics and Visual Computing (CGVC)},
editor = {{Tam, Gary K. L. and Vidal, Franck},
title = {{Screen Space Particle Selection}},
author = {Köster, Marcel and Krüger, Antonio},
year = {2018},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-071-0},
DOI = {10.2312/cgvc.20181208}
}