Recent advances in digitization technologies have transformed the preservation and dissemination of cultural heritage. In this vein, Neural Radiance Fields (NeRF) have emerged as a leading technology for 3D digitization, delivering representations with exceptional realism. However, existing methods struggle to accurately model anisotropic specular surfaces, typically observed, for example, on brushed metals. In this work, we introduce ShinyNeRF, a novel framework capable of handling both isotropic and anisotropic reflections. Our method is capable of jointly estimating surface normals, tangents, specular concentration, and anisotropy magnitudes of an Anisotropic Spherical Gaussian (ASG) distribution, by learning an approximation of the outgoing radiance as an encoded mixture of isotropic von Mises-Fisher (vMF) distributions. Experimental results show that ShinyNeRF not only achieves state-of-the-art performance on digitizing anisotropic specular reflections, but also offers plausible physical interpretations and editing of material properties compared to existing methods.

翻译：近年来，数字化技术的进步极大地推动了文化遗产的保护与传播。在此背景下，神经辐射场（NeRF）已成为三维数字化的前沿技术，能够提供具有卓越真实感的场景表示。然而，现有方法难以准确建模各向异性高光表面，例如在拉丝金属上常见的反射特性。本研究提出ShinyNeRF，一种能够同时处理各向同性与各向异性反射的新型框架。该方法通过学习出射辐射度的近似表示——将其编码为各向同性冯·米塞斯-费希尔（vMF）分布的混合形式，能够联合估计表面法线、切向量、高光集中度及各向异性球面高斯（ASG）分布的各向异性强度。实验结果表明，ShinyNeRF不仅在数字化各向异性高光反射方面达到最先进的性能，相较于现有方法，还能为材质属性提供更合理的物理解释与编辑功能。