Neural Cameras: Learning Camera Characteristics for Coherent Mixed Reality Rendering

David Mandl, Peter M. Roth, Tobias Langlotz, Christoph Ebner, Shohei Mori, Stefanie Zollmann, Peter Mohr, Denis Kalkofen

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung


Coherent rendering is important for generating plausible Mixed Reality presentations of virtual objects within a user’s real-world environment. Besides photo-realistic rendering and correct lighting, visual coherence requires simulating the imaging system that is used to capture the real environment. While existing approaches either focus on a specific camera or a specific component of the imaging system, we introduce Neural Cameras, the first approach that jointly simulates all major components of an arbitrary modern camera using neural networks. Our system allows for adding new cameras to the framework by learning the visual properties from a database of images that has been captured using the physical camera. We present qualitative and quantitative results and discuss future direction for research that emerge from using Neural Cameras.
Titel2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)
Herausgeber (Verlag)IEEE Publications
ISBN (Print)978-1-7281-9777-7
PublikationsstatusVeröffentlicht - 8 Okt. 2021
Veranstaltung2021 IEEE International Symposium on Mixed and Augmented Reality: ISMAR 2021 - Bari, Italien
Dauer: 4 Okt. 20218 Okt. 2021


Konferenz2021 IEEE International Symposium on Mixed and Augmented Reality


  • Visualization
  • Runtime
  • Three-dimensional displays
  • Pipelines
  • Mixed reality
  • Coherence
  • Tools

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