View-dependent appearance modeling remains a challenging problem in novel-view synthesis and reconstruction. Accurately representing complex angular effects often requires substantial memory and computational resources. For new learning-based methods, a common approach is to rely on SH. However, capturing high-frequency phenomena such as specular reflections demands high-order expansions, which increase memory usage and computational cost. Consequently, most methods employ low-order SH, which limits the ability to model complex view-dependent effects, resulting in overly smooth or diffuse representations. To address these limitations, we systematically evaluate a wide range of spherical functions in the context of scene reconstruction. Some of them are introduced to graphics and computer vision for the first time in this paper. Based on the insights from the experiment, we develop a novel spherical formulation, the Normalized Anisotropic Spherical Gabor function that enables efficient modeling and learning of high-frequency appearance effects while maintaining compact representation. Compared to existing approaches, our function achieves higher-quality reconstruction of view-dependent phenomena such as glints, while being up to five times more memory-efficient and more efficient to evaluate. We validate its performance in radiance-field reconstruction tasks.

翻译：视角相关的外观建模在新视角合成与重建中仍然是一个具有挑战性的问题。精确表示复杂的角度效应通常需要大量的内存和计算资源。对于新的基于学习的方法，一种常见做法是依赖球谐函数。然而，捕捉镜面反射等高頻现象需要高阶展开，这增加了内存使用和计算成本。因此，大多数方法采用低阶球谐函数，这限制了建模复杂视角相关效应的能力，导致表示过于平滑或漫反射化。为了解决这些局限性，我们系统评估了场景重建任务中多种球面函数。其中一些函数是本文首次引入图形学与计算机视觉领域。基于实验洞察，我们提出了一种新的球面公式——归一化各向异性球面加伯函数，该函数能够高效建模和学习高频率外观效应，同时保持紧凑的表示。与现有方法相比，我们的函数在实现更高品质重建视角相关现象（如闪光）的同时，内存效率提升高达五倍，且评估效率更高。我们在辐射场重建任务中验证了其性能。