Image and Distribution Based Volume Rendering for Large Data Sets

Ko Chih Wang, Naeem Shareef, Han Wei Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)


Analyzing scientific datasets created from simulations on modern supercomputers is a daunting challenge due to the fast pace at which these datasets continue to grow. Low cost post analysis machines used by scientists to view and analyze these massive datasets are severely limited by their deficiencies in storage bandwidth, capacity, and computational power. Trying to simply move these datasets to these platforms is infeasible. Any approach to view and analyze these datasets on post analysis machines will have to effectively address the inevitable problem of data loss. Image based approaches are well suited for handling very large datasets on low cost platforms. Three challenges with these approaches are how to effectively represent the original data with minimal data loss, analyze the data in regards to transfer function exploration, which is a key analysis tool, and quantify the error from data loss during analysis. We present a novel image based approach using distributions to preserve data integrity. At each view sample, view dependent data is summarized at each pixel with distributions to define a compact proxy for the original dataset. We present this representation along with how to manipulate and render large scale datasets on post analysis machines. We show that our approach is a good trade off between rendering quality and interactive speed and provides uncertainty quantification for the information that is lost.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE Pacific Visualization Symposium, PacificVis 2018
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)9781538614242
Publication statusPublished - 2018 May 25
Externally publishedYes
Event11th IEEE Pacific Visualization Symposium, PacificVis 2018 - Kobe, Japan
Duration: 2018 Apr 102018 Apr 13

Publication series

NameIEEE Pacific Visualization Symposium
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773


Conference11th IEEE Pacific Visualization Symposium, PacificVis 2018


  • Distribution based
  • big data
  • volume rendering

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software


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