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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Compact 3D Gaussian Representation for Radiance Field · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2311.13681/</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/2311.13681/feed.xml" rel="self" type="application/rss+xml" /><item><title>Compact 3D Gaussian Representation for Radiance Field — mentioned by peppertree</title><link>https://news.ycombinator.com/item?id=39911735</link><guid isPermaLink="false">urn:hackerpapers:mention:2311.13681:39911735</guid><pubDate>Tue, 02 Apr 2024 22:53:06 GMT</pubDate><description>&lt;p&gt;Here is a paper if you are interested. https://arxiv.org/pdf/2311.13681.pdf The key is not to compress but to leverage the property of neural radiance fields and optimize for entropy. I suspect NERF can yield more compact storage since it&amp;#x27;s volumetric. Not sure what you mean by &amp;quot;unacceptably low up close&amp;quot;. Most GS demos don&amp;#x27;t have LoD lol.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2311.13681/"&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>
