Abstract
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.
Abstract (translated)
尽管在神经元辐射场（NeRFs）方面取得了显著的进步，但渲染仍然可能存在混叠和模糊伪影，因为仍然难以有效地和高效地描述由透镜成形过程产生的各向同性区域是一个基本挑战。本文引入了一种Ripmap编码的Platonic固体表示来精确和有效地特征化3D各向同性区域，实现高保真度的抗混叠渲染。我们方法的核心是两个关键组件：Platonic固体投影和Ripmap编码。Platonic固体投影将3D空间分解为某个Platonic固体的独特表面，使得各向同性3D区域可以投影到具有可区分特征的平面上。同时，每个面都用Ripmap编码编码，该编码是由预处理学习到的特征网格进行非各向同性预处理，以实现对投影各向同性区域的准确和高效编码。在既定的合成数据集和一个新的捕捉到的现实世界数据集上进行广泛的实验证明，我们的Rip-NeRF达到最先进的渲染质量，尤其是在重复结构和纹理的细小细节方面表现出色，同时保持相对较快的训练时间。
URL
https://arxiv.org/abs/2405.02386
