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| − | [[File:DCIO2021-Logo.png|right|250px|link=DC_I/O_2022]] | + | [[File:DCIO2022-Logo.png|right|200px|link=DC_I/O_2022]] |
| | [[Category:DCIO]] | | [[Category:DCIO]] |
| − | [[Category:DCIO2021]] | + | [[Category:DCIO2022]] |
| | [[Category:DCIO Proceedings]] | | [[Category:DCIO Proceedings]] |
| | [[Category:Conferences]] | | [[Category:Conferences]] |
| | [[Category:Book]] | | [[Category:Book]] |
| − | [[DC I/O 2021]] Keynote by [https://jamestompkin.com// JAMES TOMPKIN]. https://doi.org/10.47330/DCIO.2022.AXBL8798 | + | [[DC I/O 2022]] Keynote by [https://jamestompkin.com// James Tompkin]. https://doi.org/10.47330/DCIO.2022.AXBL8798 | [[File:VideoRecord-Icon.png |Left|22px|link=https://youtu.be/tmuQJCVKTuI]] | [[File:Paper-Icon.png |Left|30px|link=https://www.dropbox.com/]] | [[File:Poster-Icon.png |Left|30px|link=https://www.dropbox.com/]] | [[File:Slides-Icon.png |Left|30px|link=https://www.dropbox.com/]] |
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| − | [[File:DCIO2021 F-Berar.png|center|800px]]
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| − | =Abstract=
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| | | | |
| − | =Presentation=
| + | [[File:DCIO2022 S1 1 J-Tompkin.png|center|800px]] |
| − | [[File:YouTube.png |Left|50px|link=https://www.youtube.com/watch?v=T_wkB680Hrw]] [https://www.youtube.com/watch?v=T_wkB680Hrw Video Recording]. | + | |
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| − | =Conference Paper= | + | =Abstract= |
| − | [[File:PDF-Icon.png |Left|50px|link=https://www.dropbox.com/s/99ql59hn2ngyb11/DCIO2021_S3-3_F.Berar.pdf?dl=0]] [https://www.dropbox.com/s/99ql59hn2ngyb11/DCIO2021_S3-3_F.Berar.pdf?dl=0 Conference Paper].
| + | 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. |
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| | =Keywords= | | =Keywords= |
| − | [[AI]], [[Architect]], [[Creativity]], [[Displacement]], [[Intelligence]], [[Human]], [[Machine]], [[Software]], [[Utilitarianism]], [[Value]] | + | [[AI]], [[Architect]], [[Neural Fields]]. |
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| − | =Reference=
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| − | DOI: https://doi.org/10.47330/DCIO.2022.AXBL8798
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| | =Bibliography= | | =Bibliography= |
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| − | *Weinstein, J., 2004. The Companion Species Manifesto: Dogs, People, and Significant Otherness Donna Haraway. Anthrozoös 17, 186–192. https://doi.org/10.2752/089279304786991828.
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| − | *Zumthor, P., 2010. Thinking Architecture, 03 edition. Birkhauser, Basel
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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.
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