NeRFahedron: A Primitive for Animatable Neural Rendering with Interactive Speed

Zackary P.T. Sin, Peter H.F. Ng, Hong Va Leong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Neural rendering is an exciting topic injecting machine learning methodologies into the classical computer graphics rendering pipeline. Although recent works have achieved remarkable fidelity, discussion on how to enable it for interactive scenarios like video games seems to be lacking. Aside from an editable 3D model and UV-mapping, an interactive application will demand the neural rendering to handle animatable 3D content with interactive speed. This is currently a gap in neural rendering and our solution to this problem is a novel neural rendering pipeline involving a primitive named NeRFahedron. It localizes a NeRF field and as such effectively reduces the number of expensive network sampling operations to improve speed. Our pipeline involves tetrahedron rasterization, localized ray marching and near-surface particle sampling. The result is a method that can enable animatable content for neural rendering with interactive speed, which has been shown to be competitive in rendering animation. We will also showcase its ability to enable interactive applications via a real-time demo.

Original languageEnglish
Article number2
Pages (from-to)1-20
JournalProceedings of the ACM on Computer Graphics and Interactive Techniques
Volume6
Issue number1
DOIs
Publication statusPublished - 16 May 2023

Keywords

  • animatable content
  • interactive
  • nerfahedron
  • neural rendering
  • neural volume rendering
  • real-time rendering

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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