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 this https URL .

利用基于网格的表示和频域表示的频谱 MipAA 方法，通过利用尺度特定的低通滤波器（LPF）和可学习的频谱遮罩，在保留重要细节的同时有效消除锯齿因素，以解决 Neural Radiance Fields 在生成 3D 场景和渲染不同相机距离下的图像时的走样问题。