Neural Fields for Scalable Scene Reconstruction

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DC I/O 2021 Keynote by JAMES TOMPKIN. https://doi.org/10.47330/DCIO.2022.AXBL8798
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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.

Presentation

Left Video Recording.

Conference Slides

Left Conference Paper.

Keywords

AI, Architect, Creativity, Displacement, Intelligence, Human, Machine, Software, Utilitarianism, Value

Reference

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

Bibliography

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  • Arendt, H., 1998. The Human Condition, 2nd ed. The University of Chicago Press, Chicago.
  • Beavers, A.F., 2002. Phenomenology and Artificial Intelligence. Metaphilosophy 33, 70–82.
  • Berkowitz, R., 2018. The Singularity and the Human Condition. Philosophy Today; Charlottesville 62, 337–355.
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