Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields --- ICCV 2023 Talk
Ғылым және технология
Project Page: jonbarron.info/zipnerf/
Abstract:
Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density. However, these grid-based approaches lack an explicit understanding of scale and therefore often introduce aliasing, usually in the form of jaggies or missing scene content. Anti-aliasing has previously been addressed by mip-NeRF 360, which reasons about sub-volumes along a cone rather than points along a ray, but this approach is not natively compatible with current grid-based techniques. We show how ideas from rendering and signal processing can be used to construct a technique that combines mip-NeRF 360 and grid-based models such as Instant NGP to yield error rates that are 8%-77% lower than either prior technique, and that trains 24x faster than mip-NeRF 360.
Пікірлер: 8
That's a damn nice graphical explanation!
This is unreal. So impressive. Thank you for your work!
We’re so back
how to use this?
Crazy impressive! Approximately many input images were needed to render the movies at the end?
@jon_barron
10 ай бұрын
Hey thanks Mike! Those results are around a thousand images: someone walking through the space holding down the shutter button on a DSLR, waving it around. It's a lot of images but it's surprisingly fast
@mene2172
3 ай бұрын
@@jon_barronso these are not frames grabbed from a video? Are they hi-res images?
Please apply this to Google Street View.