<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SemanticPaint: A Framework for the Interactive Segmentation of 3D Scenes · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1510.03727/</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/1510.03727/feed.xml" rel="self" type="application/rss+xml" /><item><title>SemanticPaint: A Framework for the Interactive Segmentation of 3D Scenes — mentioned by edran</title><link>https://news.ycombinator.com/item?id=12255980</link><guid isPermaLink="false">urn:hackerpapers:mention:1510.03727:12255980</guid><pubDate>Tue, 09 Aug 2016 16:58:20 GMT</pubDate><description>&lt;p&gt;We can reasonably assume it&amp;#x27;s nothing more complicated than what you can do using a combination of machine learning libraries and OpenCV (however if they have instead some new technique, I hope to find a paper from them in a few months :) ). EDIT: Adding more details. If you are looking for similar ideas, you should read papers in the area of object-class segmentation / classification[0][1][2], and generic supervised learning. [0] https://arxiv.org/abs/1510.03727 [1] https://www.microsoft.com/en-us/research/publication/object-... [2] https://www.ais.uni-bonn.de/papers/DAGM_NC2_2011_Schulz.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1510.03727/"&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>
