{"id":21082,"date":"2025-02-01T14:40:31","date_gmt":"2025-02-01T13:40:31","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=21082"},"modified":"2025-02-21T18:12:00","modified_gmt":"2025-02-21T17:12:00","slug":"lumigauss-relightable-gaussian-splatting-in-the-wild","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/lumigauss-relightable-gaussian-splatting-in-the-wild\/","title":{"rendered":"LumiGauss: Relightable Gaussian Splatting in the Wild"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-joanna-kaleta-nbsp-kacper-kania-nbsp-tomasz-trzcinski-nbsp-marek-kowalski\">Joanna Kaleta,&nbsp;Kacper Kania,&nbsp;Tomasz Trzcinski,&nbsp;Marek Kowalski<\/h2>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Separating lighting from geometry using unconstrained photo collections is a challenging task. Many previous attempts have either compromised the quality of the final output or suffered from slow training and inference speeds, raising doubts about their practicality. We introduce&nbsp;<strong>LumiGauss<\/strong>, a technique designed for 3D scene reconstruction using&nbsp;<strong>2D Gaussian Splatting<\/strong>. Our approach achieves high-quality reconstructions while enabling the synthesis of realistic lighting under novel environmental maps. This solution would be highly advantageous, as it could drastically cut down the extensive manual effort required to create sophisticated 3D assets, which often takes several days.<br>LumiGauss leverages&nbsp;<strong>precomputed radiance transfer<\/strong>, allowing seamless integration with graphics engines. Our&nbsp; representation also enables accurate&nbsp;<strong>shadow modeling<\/strong>, significantly enhancing the realism of generated renderings. We validate our method on the&nbsp;<strong>NeRF-OSR dataset<\/strong>, demonstrating its accuracy. Additionally, LumiGauss exhibits improved&nbsp;<strong>efficiency<\/strong>&nbsp;compared to baseline approaches. Access our code here:&nbsp;<a href=\"https:\/\/github.com\/joaxkal\/lumigauss\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/joaxkal\/lumigauss<\/a><\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>:&nbsp;Joanna Kaleta,&nbsp;Kacper Kania,&nbsp;Tomasz Trzcinski,&nbsp;Marek Kowalski<\/p>\n\n\n\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.48550\/arXiv.2408.04474<\/p>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: 3D reconstruction, Environmental lighting, Gaussian Splatting, Realistic lighting synthesis, Precomputed radiance transfer, NeRF-OSR dataset, LumiGauss<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/github.com\/joaxkal\/lumigauss\" target=\"_self\"  class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>conference manuscript: https:\/\/arxiv.org\/abs\/2408.04474, 2025<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[17],"class_list":["post-21082","research","type-research","status-publish","hentry","research_type-publications","research_team-health-informatics-group-higs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - 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Many previous attempts have either compromised the quality of the final output or suffered from slow training and inference speeds, raising doubts about their practicality. We introduce&nbsp;<strong>LumiGauss<\/strong>, a technique designed for 3D scene reconstruction using&nbsp;<strong>2D Gaussian Splatting<\/strong>. Our approach achieves high-quality reconstructions while enabling the synthesis of realistic lighting under novel environmental maps. This solution would be highly advantageous, as it could drastically cut down the extensive manual effort required to create sophisticated 3D assets, which often takes several days.<br>LumiGauss leverages&nbsp;<strong>precomputed radiance transfer<\/strong>, allowing seamless integration with graphics engines. Our&nbsp; representation also enables accurate&nbsp;<strong>shadow modeling<\/strong>, significantly enhancing the realism of generated renderings. We validate our method on the&nbsp;<strong>NeRF-OSR dataset<\/strong>, demonstrating its accuracy. Additionally, LumiGauss exhibits improved&nbsp;<strong>efficiency<\/strong>&nbsp;compared to baseline approaches. Access our code here:&nbsp;<a href=\"https:\/\/github.com\/joaxkal\/lumigauss\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/joaxkal\/lumigauss<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Separating lighting from geometry using unconstrained photo collections is a challenging task. Many previous attempts have either compromised the quality of the final output or suffered from slow training and inference speeds, raising doubts about their practicality. We introduce&nbsp;<strong>LumiGauss<\/strong>, a technique designed for 3D scene reconstruction using&nbsp;<strong>2D Gaussian Splatting<\/strong>. Our approach achieves high-quality reconstructions while enabling the synthesis of realistic lighting under novel environmental maps. This solution would be highly advantageous, as it could drastically cut down the extensive manual effort required to create sophisticated 3D assets, which often takes several days.<br>LumiGauss leverages&nbsp;<strong>precomputed radiance transfer<\/strong>, allowing seamless integration with graphics engines. Our&nbsp; representation also enables accurate&nbsp;<strong>shadow modeling<\/strong>, significantly enhancing the realism of generated renderings. We validate our method on the&nbsp;<strong>NeRF-OSR dataset<\/strong>, demonstrating its accuracy. Additionally, LumiGauss exhibits improved&nbsp;<strong>efficiency<\/strong>&nbsp;compared to baseline approaches. Access our code here:&nbsp;<a href=\"https:\/\/github.com\/joaxkal\/lumigauss\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/joaxkal\/lumigauss<\/a><\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"40px","epAnimationGeneratedClass":"edplus_anim-J6a2RY","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-VoAvEk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>:&nbsp;Joanna Kaleta,&nbsp;Kacper Kania,&nbsp;Tomasz Trzcinski,&nbsp;Marek Kowalski<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>:&nbsp;Joanna Kaleta,&nbsp;Kacper Kania,&nbsp;Tomasz Trzcinski,&nbsp;Marek Kowalski<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-VoAvEk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.48550\/arXiv.2408.04474<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.48550\/arXiv.2408.04474<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-VoAvEk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: 3D reconstruction, Environmental lighting, Gaussian Splatting, Realistic lighting synthesis, Precomputed radiance transfer, NeRF-OSR dataset, LumiGauss<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: 3D reconstruction, Environmental lighting, Gaussian Splatting, Realistic lighting synthesis, Precomputed radiance transfer, NeRF-OSR dataset, LumiGauss<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"40px","epAnimationGeneratedClass":"edplus_anim-J6a2RY","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/github.com\/joaxkal\/lumigauss","button_style":"primary","target":"_self","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]},{"blockName":"core\/spacer","attrs":{"epAnimationGeneratedClass":"edplus_anim-J6a2RY","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/21082","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":17,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/21082\/revisions"}],"predecessor-version":[{"id":21586,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/21082\/revisions\/21586"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=21082"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=21082"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=21082"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}