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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2311.11221/</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/2311.11221/feed.xml" rel="self" type="application/rss+xml" /><item><title>GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise — mentioned by desdenova</title><link>https://news.ycombinator.com/item?id=41827006</link><guid isPermaLink="false">urn:hackerpapers:mention:2311.11221:41827006</guid><pubDate>Sun, 13 Oct 2024 11:18:54 GMT</pubDate><description>&lt;p&gt;I think the closest we have right now is 3D gaussian splatting. So far it&amp;#x27;s only been used to train a scene from photographs from multiple angles and rebuild it volumetrically by adjusting densities in a point-cloud. But it might be possible to train a model on multiple different scenes, and perform diffusion on a random point cloud to generate new scenes. Rendering a point cloud in real time is also very efficient, so it could be used to create insanely realistic game worlds instead of polygonal geometry. It seems someone already thought of that: https://ar5iv.labs.arxiv.org/html/2311.11221&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2311.11221/"&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>
