  Neural Radiance Fields (NeRF) have shown remarkable success in representing
3D scenes and generating novel views. However, they often struggle with
aliasing artifacts, especially when rendering images from different camera
distances from the training views. To address the issue, Mip-NeRF proposed
using volumetric frustums to render a pixel and suggested integrated positional
encoding (IPE). While effective, this approach requires long training times due
to its reliance on MLP architecture. In this work, we propose a novel
anti-aliasing technique that utilizes grid-based representations, usually
showing significantly faster training time. In addition, we exploit
frequency-domain representation to handle the aliasing problem inspired by the
sampling theorem. The proposed method, FreqMipAA, utilizes scale-specific
low-pass filtering (LPF) and learnable frequency masks. Scale-specific low-pass
filters (LPF) prevent aliasing and prioritize important image details, and
learnable masks effectively remove problematic high-frequency elements while
retaining essential information. By employing a scale-specific LPF and
trainable masks, FreqMipAA can effectively eliminate the aliasing factor while
retaining important details. We validated the proposed technique by
incorporating it into a widely used grid-based method. The experimental results
have shown that the FreqMipAA effectively resolved the aliasing issues and
achieved state-of-the-art results in the multi-scale Blender dataset. Our code
is available at https://github.com/yi0109/FreqMipAA .
