Difference between revisions of "Neural Fields for Scalable Scene Reconstruction"
From Design Computation
Abel Maciel (talk | contribs) |
Abel Maciel (talk | contribs) |
||
| Line 1: | Line 1: | ||
[[File:DCIO2022-Logo.png|right|200px|link=DC_I/O_2022]] | [[File:DCIO2022-Logo.png|right|200px|link=DC_I/O_2022]] | ||
[[Category:DCIO]] | [[Category:DCIO]] | ||
| − | [[Category: | + | [[Category:DCIO2022]] |
[[Category:DCIO Proceedings]] | [[Category:DCIO Proceedings]] | ||
[[Category:Conferences]] | [[Category:Conferences]] | ||
Revision as of 20:42, 5 November 2022
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
Conference Slides
Keywords
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.
