{"id":2253,"date":"2026-09-21T09:23:00","date_gmt":"2026-09-21T07:23:00","guid":{"rendered":"https:\/\/www.ideas.edu.pl\/?post_type=publikacje&#038;p=2253"},"modified":"2026-09-21T10:35:14","modified_gmt":"2026-09-21T08:35:14","slug":"hypernetwork-approach-to-rapid-nerf-adaptation","status":"publish","type":"publikacje","link":"https:\/\/www.ideas.edu.pl\/en\/publikacje\/hypernetwork-approach-to-rapid-nerf-adaptation\/","title":{"rendered":"A Hypernetwork Approach to Rapid NeRF Adaptation"},"content":{"rendered":"<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D views of objects from several base images. However, NeRFs have limited generalization capabilities, meaning we need to use significant computational resources to train individual architectures for each object we want to represent. To address this issue, we propose a few-shot learning approach based on the hypernetwork paradigm that does not require gradient optimization during inference. Instead, the hypernetwork gathers information from the training data and updates universal weights. As a result, we have developed an efficient method for generating a high-quality 3D object representation from a small number of images in a single step, applicable in various use cases, such as robotic teleoperation. We confirmed this by directly comparing our method with state-of-the-art solutions.<\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">Authors: Pawe\u0142 Batorski, Dawid Malarz, Marcin Przewi\u0119\u017alikowski, Marcin Mazur, S\u0142awomir Tadeja, Przemys\u0142aw Spurek<\/p>\n\n\n\n<div style=\"height:64px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-group is-layout-grid wp-container-core-group-is-layout-5ab56a3c wp-block-group-is-layout-grid\">\n<figure class=\"wp-block-image size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"770\" height=\"398\" src=\"https:\/\/www.ideas.edu.pl\/wp-content\/uploads\/hyperplanes.webp\" alt=\"\" class=\"wp-image-2303\" srcset=\"https:\/\/www.ideas.edu.pl\/wp-content\/uploads\/hyperplanes.webp 770w, https:\/\/www.ideas.edu.pl\/wp-content\/uploads\/hyperplanes-18x9.webp 18w, https:\/\/www.ideas.edu.pl\/wp-content\/uploads\/hyperplanes-300x155.webp 300w, https:\/\/www.ideas.edu.pl\/wp-content\/uploads\/hyperplanes-766x396.webp 766w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-group is-layout-grid wp-container-core-group-is-layout-5ab56a3c wp-block-group-is-layout-grid\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>","protected":false},"template":"","nazwa-konferencji":[],"rodzaj-publikacji":[38],"rok-publikacji":[27],"class_list":["post-2253","publikacje","type-publikacje","status-publish","hentry","rodzaj-publikacji-artykul-w-czasopismie","rok-publikacji-27"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Hypernetwork approach to rapid NeRF adaptation &#8226; IDEAS Instytut Badawczy<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ideas.edu.pl\/en\/publikacje\/hypernetwork-approach-to-rapid-nerf-adaptation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hypernetwork approach to rapid NeRF adaptation &#8226; IDEAS Instytut Badawczy\" \/>\n<meta property=\"og:description\" content=\"Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from several base images. However, NeRFs have limited generalization properties, meaning we need to use significant computational resources to train individual architectures for each item we want to represent. To address this issue, we propose a few-shot learning approach based [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ideas.edu.pl\/en\/publikacje\/hypernetwork-approach-to-rapid-nerf-adaptation\/\" \/>\n<meta property=\"og:site_name\" content=\"IDEAS Instytut Badawczy\" \/>\n<meta property=\"article:modified_time\" content=\"2026-09-21T08:35:14+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.ideas.edu.pl\/wp-content\/uploads\/hyperplanes.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"770\" \/>\n\t<meta property=\"og:image:height\" content=\"398\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.ideas.edu.pl\\\/publikacje\\\/hypernetwork-approach-to-rapid-nerf-adaptation\\\/\",\"url\":\"https:\\\/\\\/www.ideas.edu.pl\\\/publikacje\\\/hypernetwork-approach-to-rapid-nerf-adaptation\\\/\",\"name\":\"Hypernetwork approach to rapid NeRF adaptation &#8226; 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