Efficient Raycasting of Volumetric Depth Images for Remote Visualization of Large Volumes at High Frame Rates.

First Authors Aryaman Gupta
Authors Aryaman Gupta, Ulrik G√ľnther, 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
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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.
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DOI 10.1109/PacificVis56936.2023.00014
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WebOfScience Link WOS:001016413500008
Alternative Full Text URL https://arxiv.org/pdf/2206.08660.pdf
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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
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Funding Programme ScaDS.AI
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