<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Computer Vision on giacolees - Tech Blog</title><link>https://giacolees.github.io/tags/computer-vision/</link><description>Recent content in Computer Vision on giacolees - Tech Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 16 May 2026 12:32:26 +0200</lastBuildDate><atom:link href="https://giacolees.github.io/tags/computer-vision/index.xml" rel="self" type="application/rss+xml"/><item><title>Radiance Fields: From NeRF to Gaussian Splatting</title><link>https://giacolees.github.io/posts/radiance-fields/</link><pubDate>Sat, 16 May 2026 12:32:26 +0200</pubDate><guid>https://giacolees.github.io/posts/radiance-fields/</guid><description>TL;DR Every scene has a plenoptic function describing every ray of light passing through it — radiance fields are just different ways to approximate and query it. Vanilla NeRF bakes the whole scene into an MLP and integrates densities along each ray, which is data-efficient but brutally slow (~78 billion MLP evaluations per training run). Instant-NGP moves most of that work into a multi-resolution hash table, shrinking the MLP to a tiny decoder.</description></item></channel></rss>