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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1907.01341/</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/1907.01341/feed.xml" rel="self" type="application/rss+xml" /><item><title>Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer — mentioned by yorwba</title><link>https://news.ycombinator.com/item?id=22840727</link><guid isPermaLink="false">urn:hackerpapers:mention:1907.01341:22840727</guid><pubDate>Sat, 11 Apr 2020 09:49:07 GMT</pubDate><description>&lt;p&gt;&amp;gt; So basically, if you have impossible to get input data, this network can do its magic. Except they evidently got the input data for the examples in the paper, so it can&amp;#x27;t be impossible to get. They cite at least two different methods for adding depth information to a single image to generate the necessary RGBD data, different views of which can then be rendered with their inpainting applied: - https://arxiv.org/abs/1907.01341 - https://research.cs.cornell.edu/megadepth/&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1907.01341/"&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>
