SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering

Handle URI:
http://hdl.handle.net/10754/625462
Title:
SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering
Authors:
Hadwiger, Markus ( 0000-0003-1239-4871 ) ; Al-Awami, Ali K. ( 0000-0002-8725-1958 ) ; Beyer, Johanna; Agus, Marco; Pfister, Hanspeter
Abstract:
Recent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently. (2) The classification of space into empty and non-empty regions changes frequently, because the user or the evaluation of an interactive query activate a different set of objects, which makes it unfeasible to pre-compute a well-adapted space subdivision. We describe the novel SparseLeap method for efficient empty space skipping in very large volumes, even around fine structures. The main performance characteristic of SparseLeap is that it moves the major cost of empty space skipping out of the ray-casting stage. We achieve this via a hybrid strategy that balances the computational load between determining empty ray segments in a rasterization (object-order) stage, and sampling non-empty volume data in the ray-casting (image-order) stage. Before ray-casting, we exploit the fast hardware rasterization of GPUs to create a ray segment list for each pixel, which identifies non-empty regions along the ray. The ray-casting stage then leaps over empty space without hierarchy traversal. Ray segment lists are created by rasterizing a set of fine-grained, view-independent bounding boxes. Frame coherence is exploited by re-using the same bounding boxes unless the set of active objects changes. We show that SparseLeap scales better to large, sparse data than standard octree empty space skipping.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Visual Computing Center (VCC)
Citation:
Hadwiger M, Al-Awami AK, Beyer J, Agus M, Pfister H (2017) SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering. IEEE Transactions on Visualization and Computer Graphics: 1–1. Available: http://dx.doi.org/10.1109/tvcg.2017.2744238.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Visualization and Computer Graphics
Issue Date:
28-Aug-2017
DOI:
10.1109/tvcg.2017.2744238
Type:
Article
ISSN:
1077-2626
Sponsors:
We thank the anonymous reviewers for their insightful comments and for pointing us to related work. We thank John Keyser for the ‘KESM Mouse Brain’ data [31]. ‘Dreh Sensor’ courtesy of Siemens Healthcare, Components and Vacuum Technology, Imaging Solutions; reconstructed by the Siemens OEM reconstruction API CERA TXR (Theoretically Exact Reconstruction). This work was supported by funding from King Abdullah University of Science and Technology (KAUST) and KAUST award OSR-2015- CCF-2533-01.
Additional Links:
http://ieeexplore.ieee.org/document/8017589/
Appears in Collections:
Articles; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHadwiger, Markusen
dc.contributor.authorAl-Awami, Ali K.en
dc.contributor.authorBeyer, Johannaen
dc.contributor.authorAgus, Marcoen
dc.contributor.authorPfister, Hanspeteren
dc.date.accessioned2017-09-14T06:03:52Z-
dc.date.available2017-09-14T06:03:52Z-
dc.date.issued2017-08-28en
dc.identifier.citationHadwiger M, Al-Awami AK, Beyer J, Agus M, Pfister H (2017) SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering. IEEE Transactions on Visualization and Computer Graphics: 1–1. Available: http://dx.doi.org/10.1109/tvcg.2017.2744238.en
dc.identifier.issn1077-2626en
dc.identifier.doi10.1109/tvcg.2017.2744238en
dc.identifier.urihttp://hdl.handle.net/10754/625462-
dc.description.abstractRecent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently. (2) The classification of space into empty and non-empty regions changes frequently, because the user or the evaluation of an interactive query activate a different set of objects, which makes it unfeasible to pre-compute a well-adapted space subdivision. We describe the novel SparseLeap method for efficient empty space skipping in very large volumes, even around fine structures. The main performance characteristic of SparseLeap is that it moves the major cost of empty space skipping out of the ray-casting stage. We achieve this via a hybrid strategy that balances the computational load between determining empty ray segments in a rasterization (object-order) stage, and sampling non-empty volume data in the ray-casting (image-order) stage. Before ray-casting, we exploit the fast hardware rasterization of GPUs to create a ray segment list for each pixel, which identifies non-empty regions along the ray. The ray-casting stage then leaps over empty space without hierarchy traversal. Ray segment lists are created by rasterizing a set of fine-grained, view-independent bounding boxes. Frame coherence is exploited by re-using the same bounding boxes unless the set of active objects changes. We show that SparseLeap scales better to large, sparse data than standard octree empty space skipping.en
dc.description.sponsorshipWe thank the anonymous reviewers for their insightful comments and for pointing us to related work. We thank John Keyser for the ‘KESM Mouse Brain’ data [31]. ‘Dreh Sensor’ courtesy of Siemens Healthcare, Components and Vacuum Technology, Imaging Solutions; reconstructed by the Siemens OEM reconstruction API CERA TXR (Theoretically Exact Reconstruction). This work was supported by funding from King Abdullah University of Science and Technology (KAUST) and KAUST award OSR-2015- CCF-2533-01.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/8017589/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectEmpty Space Skippingen
dc.subjectVolume Renderingen
dc.subjectSegmented Volume Dataen
dc.subjectHybrid Image/Object-Order Approachesen
dc.titleSparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Renderingen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.identifier.journalIEEE Transactions on Visualization and Computer Graphicsen
dc.eprint.versionPost-printen
dc.contributor.institutionJohn A. Paulson School of Engineering and Applied Sciences at Harvard University, Cambridge, MA, USAen
kaust.authorHadwiger, Markusen
kaust.authorAl-Awami, Ali K.en
kaust.authorAgus, Marcoen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.