Difference between revisions of "Neural Fields for Scalable Scene Reconstruction"
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| − | [[DC I/O 2022]] Keynote by [https://jamestompkin.com// James Tompkin]. https://doi.org/10.47330/DCIO.2022.AXBL8798 | Watch [[File:VideoRecord-Icon.png |Left|22px|link=https://youtu.be/tmuQJCVKTuI]] | [[File:Paper-Icon.png |Left| | + | [[DC I/O 2022]] Keynote by [https://jamestompkin.com// James Tompkin]. https://doi.org/10.47330/DCIO.2022.AXBL8798 | Watch [[File:VideoRecord-Icon.png |Left|22px|link=https://youtu.be/tmuQJCVKTuI]] | [[File:Paper-Icon.png |Left|26px|link=https://www.dropbox.com/]] | [[File:Poster-Icon.png |Left|26px|link=https://www.dropbox.com/]] | [[File:Slides-Icon.png |Left|26px|link=https://www.dropbox.com/]] |
Revision as of 02:12, 21 March 2023
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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
Bibliography
- Anderson, T.T., 2011. Complicating Heidegger and the Truth of Architecture. The Journal of Aesthetics and Art Criticism 69, 69–79.
