Deferred Neural Rendering for View Extrapolation

Tobias Bertel, Yusuke Tomoto, Srinivas Rao, Rodrigo Ortiz-Cayon, Stefan Holzer, Christian Richardt

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

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Image-based rendering methods that support visually pleasing specular surface reflections require accurate surface geometry and a large number of input images. Recent advances in neural scene representations show excellent visual quality while requiring only imperfect mesh proxies or no surface-based proxies at all. While providing state-of-the-art visual quality, the inference time of learned models is usually too slow for interactive applications. While using a casually captured circular video sweep as input, we extend Deferred Neural Rendering to extrapolate smooth viewpoints around specular objects like a car.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 665992
Original languageEnglish
Title of host publicationSA '20 Posters: SIGGRAPH Asia 2020 Posters
PublisherAssociation for Computing Machinery
Number of pages2
Publication statusPublished - 31 Dec 2020
EventACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia - Online, Online, Korea, Republic of
Duration: 4 Dec 202013 Dec 2020
Conference number: 13


ConferenceACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia
Abbreviated titleSIGGRAPH Asia
Country/TerritoryKorea, Republic of
Internet address


  • novel-view synthesis
  • surface light field
  • extrapolation


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