Despite significant advancements in Neural Radiance Fields (NeRFs), the renderings may still suffer from aliasing and blurring artifacts, since it remains a fundamental challenge to effectively and efficiently characterize anisotropic areas induced by the cone-casting procedure. This paper introduces a Ripmap-Encoded Platonic Solid representation to precisely and efficiently featurize 3D anisotropic areas, achieving high-fidelity anti-aliasing renderings. Central to our approach are two key components: Platonic Solid Projection and Ripmap encoding. The Platonic Solid Projection factorizes the 3D space onto the unparalleled faces of a certain Platonic solid, such that the anisotropic 3D areas can be projected onto planes with distinguishable characterization. Meanwhile, each face of the Platonic solid is encoded by the Ripmap encoding, which is constructed by anisotropically pre-filtering a learnable feature grid, to enable featurzing the projected anisotropic areas both precisely and efficiently by the anisotropic area-sampling. Extensive experiments on both well-established synthetic datasets and a newly captured real-world dataset demonstrate that our Rip-NeRF attains state-of-the-art rendering quality, particularly excelling in the fine details of repetitive structures and textures, while maintaining relatively swift training times.

翻译：尽管神经辐射场（NeRFs）取得了显著进展，但由于锥体投射过程中各向异性区域的有效且高效表征仍是一个基本挑战，其渲染结果仍可能出现锯齿和模糊伪影。本文提出了一种基于Ripmap编码的柏拉图立体表示方法，能够精确且高效地特征化三维各向异性区域，从而实现高保真抗锯齿渲染。该方法的核心包含两个关键组件：柏拉图立体投影和Ripmap编码。柏拉图立体投影将三维空间分解到特定柏拉图立体的非同向面上，使得各向异性三维区域能够投影到具有区分性表征的平面上；同时，柏拉图立体的每个面均由Ripmap编码进行编码——该编码通过对可学习特征网格进行各向异性预滤波构建——从而能够通过各向异性区域采样精确且高效地特征化投影后的各向异性区域。在成熟合成数据集和新采集的真实数据集上的大量实验表明，我们的Rip-NeRF达到了最先进的渲染质量，尤其在重复结构和纹理的精细细节方面表现优异，同时保持了较快的训练速度。