Publikationen

Evaluating the Perception of Semi-Transparent Structures in Direct Volume Rendering Techniques (Best Paper Award)

Evaluating the Perception of Semi-Transparent Structures in Direct Volume Rendering Techniques (Best Paper Award)

Rickard Englund Linköping University Timo Ropinski Ulm University

Computer Graphics and Interactive Techniques in Asia, 2016

Abstract

Direct volume rendering (DVR) provides the possibility to visualize volumetric data sets as they occur in many scientific disciplines. A key benefit of DVR is that semi-transparency can be facilitated in order to convey the complexity of the visualized data. Unfortunately, semi-transparency introduces new challenges in spatial comprehension of the visualized data, as the ambiguities inherent to semi-transparent representations affect spatial comprehension. Accordingly, many visualization techniques have been introduced to enhance the spatial comprehension of DVR images. In this paper, we conduct a user evaluation in which we compare standard DVR with five visualization techniques which have been proposed to enhance the spatial comprehension of DVR images. In our study, we investigate the perceptual performance of these techniques and compare them against each other to find out which technique is most suitable for different types of data and purposes. In order to do this, a large-scale user study was conducted with 300 participants who completed a number of micro-tasks designed such that the aggregated feedback gives us insight on how well these techniques aid the end user to perceive depth and shape of objects. Within this paper we discuss the tested techniques, present the conducted study and analyze the retrieved results.

Bibtex

content_copy
@inproceedings{englund16transparency,
	title={Evaluating the Perception of Semi-Transparent Structures in Direct Volume Rendering Techniques (Best Paper Award)},
	author={Englund, Rickard and Ropinski, Timo},
	bookTitle={Proceedings of SIGGRAPH ASIA 2016, Macao, December 5-8, 2016 - Symposium on Visualization}
	year={2016},
	month={0},
	pages={9:1-9:8},
	publisher={ACM},
	editor={Chen, Wei and Weiskopf, Daniel},
}