| First Authors |
Aryaman Gupta
|
| Authors |
Aryaman Gupta, Ulrik Günther, Pietro Incardona, Guido Reina, Steffen Frey, Stefan Gumhold, Ivo F. Sbalzarini
|
| Corresponding Authors |
Aryaman Gupta
|
| Last Authors |
Ivo F. Sbalzarini
|
| Conference Proceedings Volume Title |
2023 IEEE 16TH PACIFIC VISUALIZATION SYMPOSIUM, PACIFICVIS |
| Series Title |
(IEEE Pacific Visualization Symposium) |
| Conference Name |
IEEE 16th Pacific Visualization Symposium (IEEE PacificVis) |
| Conference Location |
Seoul, Korea |
| Conference Start Date |
2023-04-18 |
| Conference End Date |
2023-04-21 |
| Chapter Number |
|
| Publisher |
IEEE |
| Conference Proceedings Editors |
|
| ISBN |
979-8-3503-2124-1 |
| First Page |
61 |
| Last Page |
70 |
| Open Access |
false |
| Print Publication Date |
2023-04-21 |
| Online Publication Date |
2023-04-21 |
| Abstract |
We present an efficient raycasting algorithm for rendering Volumetric Depth Images (VDIs), and we show how it can be used in a remote visualization setting with VDIs generated and streamed from a remote server. VDIs are compact view-dependent volume representations that enable interactive visualization of large volumes at high frame rates by decoupling viewpoint changes from expensive rendering calculations. However, current rendering approaches for VDIs struggle with achieving interactive frame rates at high image resolutions. Here, we exploit the properties of perspective projection to simplify intersections of rays with the view-dependent frustums in a VDI and leverage spatial smoothness in the volume data to minimize memory accesses. Benchmarks show that responsive frame rates can be achieved close to the viewpoint of generation for HD display resolutions, providing high-fidelity approximate renderings of Gigabyte-sized volumes. We also propose a method to subsample the VDI for preview rendering, maintaining high frame rates even for large viewpoint deviations. We provide our implementation as an extension of an established open-source visualization library. |
| PDF |
Gupta_2023_8574.pdf
(1.6 MB)
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| Cover Image |
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| Affiliated With |
CSBD, Sbalzarini
|
| Selected By |
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| Acknowledged Services |
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| Publication Status |
Published |
| Edoc Link |
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| Sfx Link |
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| DOI |
10.1109/PacificVis56936.2023.00014 |
| PubMed ID |
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| WebOfScience Link |
WOS:001016413500008 |
| Alternative Full Text URL |
https://arxiv.org/pdf/2206.08660.pdf |
| Display Publisher Download Only |
true |
| Visible On MPI-CBG Website |
true |
| PDF Downloadable |
true |
| Created By |
sbalzari |
| Added Date |
2023-06-19 |
| Last Edited By |
thuem |
| Last Edited Date |
2023-08-11 14:11:51.733 |
| Library ID |
8574 |
| Document ID |
|
| Entry Complete |
true |
| eDoc Compliant |
true |
| Include in Edoc Report |
true |
| In Pure |
true |
| Ready for eDoc Export |
false |
| Author Affiliations Complete |
false |
| Project Name |
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| Project URL |
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| Grant ID |
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| Funding Programme |
ScaDS.AI |
| Funding Organisation |
BMBF |