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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sharp Monocular View Synthesis in Less Than a Second · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2512.10685/</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/2512.10685/feed.xml" rel="self" type="application/rss+xml" /><item><title>Sharp Monocular View Synthesis in Less Than a Second — HN submission</title><link>https://news.ycombinator.com/item?id=47995037</link><guid isPermaLink="false">urn:hackerpapers:submission:2512.10685:47995037</guid><pubDate>Sun, 03 May 2026 09:14:56 GMT</pubDate><description>&lt;p&gt;Show HN: Apple&amp;#x27;s SHARP running in the browser via ONNX runtime web&lt;/p&gt;&lt;p&gt;Hi HN, author here. SHARP is Apple&amp;#x27;s recent single-image 3D Gaussian splatting model (https://arxiv.org/abs/2512.10685). Their reference code is PyTorch + a pretty heavy pipeline; I wanted to see if it could run in a browser with no server hop, so I exported the predictor to ONNX and ran it via onnxruntime-web with the WebGPU EP. What works: drop in an image, get a .ply you can download or preview live, all on your machine — your image never leaves the tab. The model is large (~2.4 GB sidecar) so first load is slow on a cold cache, but inference itself is a few seconds on a recent Mac.…&lt;/p&gt;&lt;p&gt;185 points; 46 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2512.10685/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item><item><title>Sharp Monocular View Synthesis in Less Than a Second — mentioned by neom</title><link>https://news.ycombinator.com/item?id=46402044</link><guid isPermaLink="false">urn:hackerpapers:mention:2512.10685:46402044</guid><pubDate>Sat, 27 Dec 2025 14:24:06 GMT</pubDate><description>&lt;p&gt;Examples: https://apple.github.io/ml-sharp/ Paper: https://arxiv.org/abs/2512.10685&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2512.10685/"&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>
