{"id":32474,"date":"2026-01-21T17:02:02","date_gmt":"2026-01-21T17:02:02","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/fpo-efficient-encoding-and-rendering-of-dynamic-neural-radiance-fields-by-analyzing-and-enhancing-fourier-plenoctrees\/"},"modified":"2026-06-08T13:20:42","modified_gmt":"2026-06-08T13:20:42","slug":"fpo-efficient-encoding-and-rendering-of-dynamic-neural-radiance-fields-by-analyzing-and-enhancing-fourier-plenoctrees","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/fpo-efficient-encoding-and-rendering-of-dynamic-neural-radiance-fields-by-analyzing-and-enhancing-fourier-plenoctrees\/","title":{"rendered":"FPO++: efficient encoding and rendering of dynamic neural radiance fields by analyzing and enhancing Fourier PlenOctrees"},"content":{"rendered":"<p>Fourier PlenOctrees have shown to be an efficient representation for real-time rendering of dynamic neural radiance fields (NeRF). Despite its many advantages, this method suffers from artifacts introduced by the involved compression when combining it with recent state-of-the-art techniques for training the static per-frame NeRF models. In this paper, we perform an in-depth analysis of these artifacts and leverage the resulting insights to propose an improved representation. In particular, we present a novel density encoding that adapts the Fourier-based compression to the characteristics of the transfer function used by the underlying volume rendering procedure and leads to a substantial reduction of artifacts in the dynamic model. We demonstrate the effectiveness of our enhanced Fourier PlenOctrees in the scope of quantitative and qualitative evaluations on synthetic and real-world scenes.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Fourier PlenOctrees have shown to be an efficient representation for real-time rendering of dynamic neural radiance fields (NeRF). Despite its many advantages, this method suffers from artifacts introduced by the involved compression when combining it with recent state-of-the-art techniques for training the static per-frame NeRF models. In this paper, we perform an in-depth analysis of these artifacts and leverage the resulting insights to propose an improved representation. In particular, we [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-32474","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32474","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32474\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32474"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32474"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}