Literature DB >> 24650978

Effects of VR system fidelity on analyzing isosurface visualization of volume datasets.

Bireswar Laha1, Doug A Bowman1, John J Socha1.   

Abstract

Volume visualization is an important technique for analyzing datasets from a variety of different scientific domains. Volume data analysis is inherently difficult because volumes are three-dimensional, dense, and unfamiliar, requiring scientists to precisely control the viewpoint and to make precise spatial judgments. Researchers have proposed that more immersive (higher fidelity) VR systems might improve task performance with volume datasets, and significant results tied to different components of display fidelity have been reported. However, more information is needed to generalize these results to different task types, domains, and rendering styles. We visualized isosurfaces extracted from synchrotron microscopic computed tomography (SR-μCT) scans of beetles, in a CAVE-like display. We ran a controlled experiment evaluating the effects of three components of system fidelity (field of regard, stereoscopy, and head tracking) on a variety of abstract task categories that are applicable to various scientific domains, and also compared our results with those from our prior experiment using 3D texture-based rendering. We report many significant findings. For example, for search and spatial judgment tasks with isosurface visualization, a stereoscopic display provides better performance, but for tasks with 3D texture-based rendering, displays with higher field of regard were more effective, independent of the levels of the other display components. We also found that systems with high field of regard and head tracking improve performance in spatial judgment tasks. Our results extend existing knowledge and produce new guidelines for designing VR systems to improve the effectiveness of volume data analysis.

Year:  2014        PMID: 24650978     DOI: 10.1109/TVCG.2014.20

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  4 in total

1.  A Virtual Reality Visualization Tool for Neuron Tracing.

Authors:  Will Usher; Pavol Klacansky; Frederick Federer; Peer-Timo Bremer; Aaron Knoll; Jeff Yarch; Alessandra Angelucci; Valerio Pascucci
Journal:  IEEE Trans Vis Comput Graph       Date:  2017-08-29       Impact factor: 4.579

2.  Immersive Analytics: Theory and Research Agenda.

Authors:  Richard Skarbez; Nicholas F Polys; J Todd Ogle; Chris North; Doug A Bowman
Journal:  Front Robot AI       Date:  2019-09-10

Review 3.  3D visualization processes for recreating and studying organismal form.

Authors:  Duncan J Irschick; Fredrik Christiansen; Neil Hammerschlag; Johnson Martin; Peter T Madsen; Jeanette Wyneken; Annabelle Brooks; Adrian Gleiss; Sabrina Fossette; Cameron Siler; Tony Gamble; Frank Fish; Ursula Siebert; Jaymin Patel; Zhan Xu; Evangelos Kalogerakis; Joshua Medina; Atreyi Mukherji; Mark Mandica; Savvas Zotos; Jared Detwiler; Blair Perot; George Lauder
Journal:  iScience       Date:  2022-08-04

4.  Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements.

Authors:  Torin McDonald; Will Usher; Nate Morrical; Attila Gyulassy; Steve Petruzza; Frederick Federer; Alessandra Angelucci; Valerio Pascucci
Journal:  IEEE Trans Vis Comput Graph       Date:  2021-01-28       Impact factor: 4.579

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.