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<oembed><version>1.0</version><provider_name>Multimodal 3D Object Detection</provider_name><provider_url>https://mscvprojects.ri.cmu.edu/2020teamf</provider_url><title>Project Overview - Multimodal 3D Object Detection</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="WHNlZoaDJW"&gt;&lt;a href="https://mscvprojects.ri.cmu.edu/2020teamf/2020/05/05/mainpage/"&gt;Project Overview&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2020teamf/2020/05/05/mainpage/embed/#?secret=WHNlZoaDJW" width="600" height="338" title="&#x201C;Project Overview&#x201D; &#x2014; Multimodal 3D Object Detection" data-secret="WHNlZoaDJW" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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</html><description>This is a capstone project with Aurora Innovation. Our advisor is Varun Ramakrishna from Aurora and Kris Kitani from CMU. Enabling the perception system to detect objects in 3D space is an important problem in autonomous driving. Existing methods can be categorized as camera-only, LiDAR-only, and multi-sensor methods. For camera-only methods, depth estimation from images &hellip; Continue reading ""</description><thumbnail_url>https://mscvprojects.ri.cmu.edu/2020teamf/wp-content/uploads/sites/36/2020/12/im1-1024x531.jpg</thumbnail_url></oembed>

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