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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Cha Zhang</author_name><author_url>https://www.microsoft.com/en-us/research/people/chazhang/</author_url><title>Generalized plenoptic sampling - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="PeE1Dhx51m"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/generalized-plenoptic-sampling/"&gt;Generalized plenoptic sampling&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/generalized-plenoptic-sampling/embed/#?secret=PeE1Dhx51m" width="600" height="338" title="&#x201C;Generalized plenoptic sampling&#x201D; &#x2014; Microsoft Research" data-secret="PeE1Dhx51m" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Image-based rendering (IBR) has become a very active research area in recent years. The optimal sampling problem for IBR has not been completely solved. In this paper, we show that theoretically, the optimal sampling efficiency can be achieved by employing the generalized periodic sampling theory with arbitrary geometry. When there is no occlusion in the [&hellip;]</description></oembed>
