让神经辐射场能真实还原金属等反光表面的细节。
ShinyNeRF: Digitizing Anisotropic Appearance in Neural Radiance Fields
- 用编码混合分布建模出射辐射，同时估计表面法线、切线和反光特性。
- 在镜面反光材质重建上达到当前最佳效果，尤其擅长处理方向性反光。
- 可解释材质属性并支持编辑，适合文化遗产数字化与影视特效制作。
近年来，数字化技术推动了文化遗产的保存与传播。在此背景下，神经辐射场（NeRF）已成为3D数字化的领先技术，能够生成高度逼真的三维表示。然而，现有方法难以准确建模各向异性镜面反射，如拉丝金属表面。本文提出ShinyNeRF，一种可处理各向同性和各向异性反射的新框架。通过学习出射辐射的编码混合分布，该方法联合估计表面法线、切线、高光集中度及各向异性幅度，基于各向异性球形高斯（ASG）分布。实验表明，ShinyNeRF不仅在各向异性镜面反射重建上达到当前最优性能，还能提供更合理的物理解释，并支持材质属性的可编辑性，优于现有方法。
原文摘要 · Abstract (English)
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.
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