TY - GEN
T1 - PathlinesExplorer — Image-based exploration of large-scale pathline fields
AU - Nagoor, Omniah H.
AU - Hadwiger, Markus
AU - Srinivasan, Madhusudhanan
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2016/3/10
Y1 - 2016/3/10
N2 - PathlinesExplorer is a novel image-based tool, which has been designed to visualize large scale pathline fields on a single computer [7]. PathlinesExplorer integrates explorable images (EI) technique [4] with order-independent transparency (OIT) method [2]. What makes this method different is that it allows users to handle large data on a single workstation. Although it is a view-dependent method, PathlinesExplorer combines both exploration and modification of visual aspects without re-accessing the original huge data. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathline segments. With this view-dependent method, it is possible to filter, color-code, and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.
AB - PathlinesExplorer is a novel image-based tool, which has been designed to visualize large scale pathline fields on a single computer [7]. PathlinesExplorer integrates explorable images (EI) technique [4] with order-independent transparency (OIT) method [2]. What makes this method different is that it allows users to handle large data on a single workstation. Although it is a view-dependent method, PathlinesExplorer combines both exploration and modification of visual aspects without re-accessing the original huge data. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathline segments. With this view-dependent method, it is possible to filter, color-code, and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.
UR - http://hdl.handle.net/10754/605228
UR - http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7429512
UR - http://www.scopus.com/inward/record.url?scp=84966283591&partnerID=8YFLogxK
U2 - 10.1109/SciVis.2015.7429512
DO - 10.1109/SciVis.2015.7429512
M3 - Conference contribution
SN - 9781467397858
SP - 159
EP - 160
BT - 2015 IEEE Scientific Visualization Conference (SciVis)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -