Title
An Automatic Deformation Approach for Occlusion Free Egocentric Data Exploration
Abstract
Occlusion management is an important task for three dimension data exploration. For egocentric data exploration, the occlusion problems, caused by the camera being too close to opaque data elements, have not been well addressed by previous studies. In this paper, we propose an automatic approach to resolve these problems and provide an occlusion free egocentric data exploration. Our system utilizes a state transition model to monitor both the camera and the data, and manages the initiation, duration, and termination of deformation with animation. Our method can be applied to multiple types of scientific datasets, including volumetric data, polygon mesh data, and particle data. We demonstrate our method with different exploration tasks, including camera navigation, isovalue adjustment, transfer function adjustment, and time varying exploration. We have collaborated with a domain expert and received positive feedback.
Year
DOI
Venue
2018
10.1109/PacificVis.2018.00035
2018 IEEE Pacific Visualization Symposium (PacificVis)
Keywords
Field
DocType
Data Deformation,Occlusion Management,Data Exploration,Egocentric Visualization
Computer vision,Data visualization,Polygon mesh,Occlusion,Task analysis,Computer science,Subject-matter expert,Transfer function,Animation,Artificial intelligence,Deformation (mechanics)
Conference
ISSN
ISBN
Citations 
2165-8765
978-1-5386-1425-9
1
PageRank 
References 
Authors
0.35
33
3
Name
Order
Citations
PageRank
Li Chen14018.72
Joachim Moortgat271.52
Han-Wei Shen32204148.60