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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2210.07372/</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/2210.07372/feed.xml" rel="self" type="application/rss+xml" /><item><title>SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds — mentioned by boulos</title><link>https://news.ycombinator.com/item?id=35837097</link><guid isPermaLink="false">urn:hackerpapers:mention:2210.07372:35837097</guid><pubDate>Sat, 06 May 2023 01:30:15 GMT</pubDate><description>&lt;p&gt;Yep! Our research team publishes a lot of work (waymo.com/research). If you&amp;#x27;re interested in Transformers and attention in particular, the excellently named Wayformer [1] paper is pretty easy to follow and SWFormer is impressive on a lot of levels [2]. The main challenge for autonomous vehicles has been (large) multi-modal inputs, while delivering real-time inference performance on the road. Many of the large-language model things you see produce cool results, but would be much too slow or expensive for a real-time, safety-critical application. [1] https://arxiv.org/abs/2207.05844 [2]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2210.07372/"&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>
