LIC-R: Line Integral Convolution Revisited

Khatereh Mohammadi, Marco Agus, Ahmad Abushaikha, Jens Schneider

2025

Abstract

We present a novel formulation of Line Integral Convolution (LIC), a fundamental method for visualizing vector fields in flow visualization. Our approach reinterprets the traditional LIC technique by leveraging a regularized, directional curvature flow along streamlines, utilizing material derivatives to achieve the desired convolution. By adopting an entirely Eulerian framework, our method eliminates the need for complex numerical integration and high-order interpolation schemes that are typically required in classical LIC algorithms. This shift not only simplifies the implementation of LIC, making it more accessible for both CPU and GPU architectures, but also significantly reduces the computational overhead. Despite these simplifications, our method maintains visual quality comparable to that of more traditional and computationally expensive approaches. Moreover, the discrete nature of our formulation makes it particularly well-suited for irregular grids and sparse data, broadening its applicability in practical settings. Through various experiments, we demonstrate that our algorithm delivers efficient and visually coherent results, offering an attractive alternative for dense flow visualization with reduced complexity.

Paper Citation

in Harvard Style

Mohammadi K., Agus M., Abushaikha A. and Schneider J. (2025). LIC-R: Line Integral Convolution Revisited. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP; ISBN 978-989-758-728-3, SciTePress, pages 875-886. DOI: 10.5220/0013131700003912

in Bibtex Style

@conference{ivapp25,
author={Khatereh Mohammadi and Marco Agus and Ahmad Abushaikha and Jens Schneider},
title={LIC-R: Line Integral Convolution Revisited},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP},
year={2025},
pages={875-886},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013131700003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP
TI - LIC-R: Line Integral Convolution Revisited
SN - 978-989-758-728-3
AU - Mohammadi K.
AU - Agus M.
AU - Abushaikha A.
AU - Schneider J.
PY - 2025
SP - 875
EP - 886
DO - 10.5220/0013131700003912
PB - SciTePress