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Freq-Mip-AA : Frequency Mip Representation for Anti-Aliasing Neural Radiance Fields

Youngin Park, Seungtae Nam, Cheul-hee Hahm, Eunbyung Park

TL;DR

The paper tackles aliasing in Neural Radiance Fields (NeRF) when rendering from varying camera distances. It introduces FreqMipAA, a frequency-domain anti-aliasing approach that operates on a grid-based representation in the $DCT$ domain with scale-specific Gaussian low-pass filters and learnable frequency masks, followed by inverse DCT to produce spatial features. The key contributions include the first frequency-domain treatment for NeRF aliasing, a training scheme combining per-scale LPF and learned masks, and achieving a best-average PSNR of 35.37 on the multi-scale Blender dataset, outperforming prior methods. This approach enables effective aliasing mitigation with grid-based NeRF, offering faster training and potential for compressible anti-aliasing models in practical rendering pipelines.

Abstract

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 .

Freq-Mip-AA : Frequency Mip Representation for Anti-Aliasing Neural Radiance Fields

TL;DR

The paper tackles aliasing in Neural Radiance Fields (NeRF) when rendering from varying camera distances. It introduces FreqMipAA, a frequency-domain anti-aliasing approach that operates on a grid-based representation in the domain with scale-specific Gaussian low-pass filters and learnable frequency masks, followed by inverse DCT to produce spatial features. The key contributions include the first frequency-domain treatment for NeRF aliasing, a training scheme combining per-scale LPF and learned masks, and achieving a best-average PSNR of 35.37 on the multi-scale Blender dataset, outperforming prior methods. This approach enables effective aliasing mitigation with grid-based NeRF, offering faster training and potential for compressible anti-aliasing models in practical rendering pipelines.

Abstract

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 .
Paper Structure (18 sections, 5 equations, 3 figures, 2 tables)

This paper contains 18 sections, 5 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Anti-aliasing
  4. Grid-based Model
  5. Anti-aliasing in NeRF
  6. Preliminaries
  7. Sampling Theorem
  8. NeRF
  9. Grid-based NeRF
  10. Method
  11. Scale-specific Low-pass filter
  12. Learnable Frequency Mask
  13. Experiments
  14. Experiment setup
  15. Results
  16. ...and 3 more sections

Figures (3)

  • Figure 1: The overall architecture of our model begins with training a shared grid in the frequency domain. This is followed by scale-specific low-pass filters designed to facilitate focus on crucial information. Subsequently, learnable frequency masks are applied to further refine frequency grids. To enhance visual clarity, the grid is shown as a basic square shape, even though it is fundamentally a vector matrix structure. The $\odot$ represents element-wise multiplication.
  • Figure 2: Scale-specific feature maps(Top) and masks(Bottom) at 1x, 1/2x, 1/4x, and 1/8x capture global structure and local details for improved picture reconstruction
  • Figure 3: A qualitative evaluation of FreqMipAA against the Ground Truth, TensoRF and Tri-MipRF on Blender dataset. The zoomed sections of images rendered at four distinct scales are displayed.