Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields
This addresses aliasing issues in 3D scene reconstruction for computer vision and graphics applications, representing an incremental improvement by integrating existing methods.
The paper tackled the problem of aliasing in grid-based Neural Radiance Fields (NeRF) by combining mip-NeRF 360 with grid-based models, resulting in error rates 8% to 77% lower than prior techniques and training 24x faster than mip-NeRF 360.
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.