liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Adaptive Sampling in Single Pass, GPU-based Raycasting of Multiresolution Volumes
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9288-5322
2006 (English)In: Proceedings Eurographics/IEEE International Workshop on Volume Graphics 2006, Boston, USA, 2006, 39-46 p.Conference paper (Other academic)
Abstract [en]

This paper presents a novel direct volume rendering technique for adaptive object- and image-space sampling density of multiresolution volumes. The raycasting is implemented entirely on the GPU in a single pass fragment program which adapts the sampling density along rays, guided by block resolutions. The multiresolution volumes are provided by a transfer function based level-of-detail scheme adaptively loading large out-of-core volumes. Adaptive image-space sampling is achieved by gathering projected basic volume block statistics for screen tiles and then allocating a level-of-detail for each tile. This combination of techniques provides a significant reduction of processing requirements while maintaining high quality rendering.

Place, publisher, year, edition, pages
2006. 39-46 p.
Keyword [en]
Viewing algorithms; Image Processing; Computer Vision
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-13947DOI: 10.2312/VG/VG06/039-046OAI: diva2:22277
Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-05-27
In thesis
1. Efficient Methods for Direct Volume Rendering of Large Data Sets
Open this publication in new window or tab >>Efficient Methods for Direct Volume Rendering of Large Data Sets
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Direct Volume Rendering (DVR) is a technique for creating images directly from a representation of a function defined over a three-dimensional domain. The technique has many application fields, such as scientific visualization and medical imaging. A striking property of the data sets produced within these fields is their ever increasing size and complexity. Despite the advancements of computing resources these data sets seem to grow at even faster rates causing severe bottlenecks in terms of data transfer bandwidths, memory capacity and processing requirements in the rendering pipeline.

This thesis focuses on efficient methods for DVR of large data sets. At the core of the work lies a level-of-detail scheme that reduces the amount of data to process and handle, while optimizing the level-of-detail selection so that high visual quality is maintained. A set of techniques for domain knowledge encoding which significantly improves assessment and prediction of visual significance for blocks in a volume are introduced. A complete pipeline for DVR is presented that uses the data reduction achieved by the level-of-detail selection to minimize the data requirements in all stages. This leads to reduction of disk I/O as well as host and graphics memory. The data reduction is also exploited to improve the rendering performance in graphics hardware, employing adaptive sampling both within the volume and within the rendered image.

The developed techniques have been applied in particular to medical visualization of large data sets on commodity desktop computers using consumer graphics processors. The specific application of virtual autopsies has received much interest, and several developed data classification schemes and rendering techniques have been motivated by this application. The results are, however, general and applicable in many fields and significant performance and quality improvements over previous techniques are shown.

Place, publisher, year, edition, pages
Institutionen för teknik och naturvetenskap, 2006
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1043
Computer Graphics, Scientific Visualization, Medical Imaging, Volume Rendering, Raycasting, Transfer Functions, Level-of-detail, Fuzzy Classification, Virtual Autopsies
National Category
Computer Science
urn:nbn:se:liu:diva-7232 (URN)91-85523-05-4 (ISBN)
Public defence
2006-10-06, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 09:15 (English)
On the defence date the status of article IX was Accepted.Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-09-22

Open Access in DiVA

No full text

Other links

Publisher's full textLink to Ph.D. thesis

Search in DiVA

By author/editor
Ljung, Patric
By organisation
Visual Information Technology and Applications (VITA)The Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 3036 hits
ReferencesLink to record
Permanent link

Direct link