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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>Neel Joshi</author_name><author_url>https://www.microsoft.com/en-us/research/people/neel/</author_url><title>Micro-Baseline Stereo - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="BXthBWQvZT"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/micro-baseline-stereo/"&gt;Micro-Baseline Stereo&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/micro-baseline-stereo/embed/#?secret=BXthBWQvZT" width="600" height="338" title="&#x201C;Micro-Baseline Stereo&#x201D; &#x2014; Microsoft Research" data-secret="BXthBWQvZT" 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>Tradeoffs exist between the baseline or distance between cameras and the difficulty of matching corresponding points in stereo and structure from motion. Smaller baselines result in reduced disparities reducing the accuracy of depth estimation. Larger baselines increase the range of observed disparities, but also increase the difficulty of finding corresponding points. In this paper, we [&hellip;]</description></oembed>
