Difference between revisions of "Neural Fields for Scalable Scene Reconstruction"

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=Abstract=
 
=Abstract=
 
Neural fields are a new (and old!) approach to solving problems over spacetime via first-order optimization of a neural network. Over the past three years, combining neural fields with classic computer graphics approaches have allowed us to make significant advances in solving computer vision problems like scene reconstruction. I will present recent work that can reconstruct indoor scenes for photorealistic interactive exploration using new scalable hybrid neural field representations. This has applications where any real-world place needs to be digitized, especially for visualization purposes.
 
Neural fields are a new (and old!) approach to solving problems over spacetime via first-order optimization of a neural network. Over the past three years, combining neural fields with classic computer graphics approaches have allowed us to make significant advances in solving computer vision problems like scene reconstruction. I will present recent work that can reconstruct indoor scenes for photorealistic interactive exploration using new scalable hybrid neural field representations. This has applications where any real-world place needs to be digitized, especially for visualization purposes.
 
=Conference Slides=
 
[[File:PDF-Icon.png |Left|50px|link=https://www.dropbox.com/]] Conference Slides.
 
  
 
=Keywords=
 
=Keywords=

Revision as of 19:24, 15 March 2023

DCIO2022-Logo.png
DC I/O 2022 Keynote by James Tompkin. https://doi.org/10.47330/DCIO.2022.AXBL8798 | Left | Left


DCIO2022 S1 1 J-Tompkin.png


Abstract

Neural fields are a new (and old!) approach to solving problems over spacetime via first-order optimization of a neural network. Over the past three years, combining neural fields with classic computer graphics approaches have allowed us to make significant advances in solving computer vision problems like scene reconstruction. I will present recent work that can reconstruct indoor scenes for photorealistic interactive exploration using new scalable hybrid neural field representations. This has applications where any real-world place needs to be digitized, especially for visualization purposes.

Keywords

AI, Architect

Reference

DOI: https://doi.org/10.47330/DCIO.2022.AXBL8798

Bibliography

  • Anderson, T.T., 2011. Complicating Heidegger and the Truth of Architecture. The Journal of Aesthetics and Art Criticism 69, 69–79.