  Despite the remarkable achievements of neural radiance fields (NeRF) in
representing 3D scenes and generating novel view images, the aliasing issue,
rendering "jaggies" or "blurry" images at varying camera distances, remains
unresolved in most existing approaches. The recently proposed mip-NeRF has
addressed this challenge by rendering conical frustums instead of rays.
However, it relies on MLP architecture to represent the radiance fields,
missing out on the fast training speed offered by the latest grid-based
methods. In this work, we present mip-Grid, a novel approach that integrates
anti-aliasing techniques into grid-based representations for radiance fields,
mitigating the aliasing artifacts while enjoying fast training time. The
proposed method generates multi-scale grids by applying simple convolution
operations over a shared grid representation and uses the scale-aware
coordinate to retrieve features at different scales from the generated
multi-scale grids. To test the effectiveness, we integrated the proposed method
into the two recent representative grid-based methods, TensoRF and K-Planes.
Experimental results demonstrate that mip-Grid greatly improves the rendering
performance of both methods and even outperforms mip-NeRF on multi-scale
datasets while achieving significantly faster training time. For code and demo
videos, please see https://stnamjef.github.io/mipgrid.github.io/.
