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Multi-view point splatting


Hübner, T; Zhang, Y; Pajarola, R (2006). Multi-view point splatting. In: GRAPHITE: Conference on Computer Graphics and Interactive Techniques in Australasia and South-East Asia, Kuala Lumpur, Malaysia, 29 November 2006 - 2 December 2006, 285-294,499.

Abstract

The fundamental drawback of current stereo and multi-view visualization
is the necessity to perform multi pass rendering (one pass for
each view) and subsequent image composition + masking for generating
multiple stereo views. Thus the rendering time increases in
general linearly with the number of views.
In this paper we introduce a new method for multi-view splatting
based on deferred blending. Our method exploits the programmability
of modern graphic processing units (GPUs) for rendering
multiple stereo views in a single rendering pass. The views are
calculated directly on the GPU including sub-pixel wavelength selective
views. We describe our algorithm precisely and provide details
about its implementation. Experimental results demonstrate
the performance advantage of our multi-view point splatting algorithm
compared to the standard multi-pass approach.

The fundamental drawback of current stereo and multi-view visualization
is the necessity to perform multi pass rendering (one pass for
each view) and subsequent image composition + masking for generating
multiple stereo views. Thus the rendering time increases in
general linearly with the number of views.
In this paper we introduce a new method for multi-view splatting
based on deferred blending. Our method exploits the programmability
of modern graphic processing units (GPUs) for rendering
multiple stereo views in a single rendering pass. The views are
calculated directly on the GPU including sub-pixel wavelength selective
views. We describe our algorithm precisely and provide details
about its implementation. Experimental results demonstrate
the performance advantage of our multi-view point splatting algorithm
compared to the standard multi-pass approach.

Citations

1 citation in Web of Science®
9 citations in Scopus®
Google Scholar™

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Additional indexing

Contributors:ACM
Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Event End Date:2 December 2006
Deposited On:24 Mar 2011 14:55
Last Modified:05 Apr 2016 14:53
Permanent URL: https://doi.org/10.5167/uzh-47725

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