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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Car Segmentation and Pose Estimation using 3D Object Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1512.06790/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1512.06790/feed.xml" rel="self" type="application/rss+xml" /><item><title>Car Segmentation and Pose Estimation using 3D Object Models — mentioned by visarga</title><link>https://news.ycombinator.com/item?id=12557772</link><guid isPermaLink="false">urn:hackerpapers:mention:1512.06790:12557772</guid><pubDate>Thu, 22 Sep 2016 15:59:26 GMT</pubDate><description>&lt;p&gt;&amp;gt; Even for 2D picture we build mental models of the scene in ways image classifiers flat out don&amp;#x27;t and can&amp;#x27;t. That&amp;#x27;s not true. Conditional Random Fields and other statistical models are being used to model spatial object relations. Example: https://arxiv.org/abs/1512.06790v2&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1512.06790/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
