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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1709.07492/</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/1709.07492/feed.xml" rel="self" type="application/rss+xml" /><item><title>Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image — mentioned by blt</title><link>https://news.ycombinator.com/item?id=17721484</link><guid isPermaLink="false">urn:hackerpapers:mention:1709.07492:17721484</guid><pubDate>Thu, 09 Aug 2018 03:34:33 GMT</pubDate><description>&lt;p&gt;there are lots of papers that attempt to reconstruct depth maps from monocular RGB images. It&amp;#x27;s not really my field but here is google scholar search for recent papers that cite a seminal older paper on the topic: https://scholar.google.com/scholar?as_ylo=2017&amp;amp;hl=en&amp;amp;as_sdt=... that should be a decent start. If you know the depth of a few pixels in the image, e.g. from a sparse keypoint-based SLAM / visual-inertial odometry system, then you can do better: https://arxiv.org/pdf/1709.07492.pdf If you already have accurate camera positions, you can use something like occupancy grid mapping or…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1709.07492/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.RO</category></item></channel></rss>
