Neural radiance fields (NeRFs) have achieved impressive view synthesis
results by learning an implicit volumetric representation from multi-view
images. To project the implicit representation into an image, NeRF employs
volume rendering that approximates the continuous integrals of rays as an
accumulation of the colors and densities of the sampled points. Although this
approximation enables efficient rendering, it ignores the direction information
in point intervals, resulting in ambiguous features and limited reconstruction
quality. In this paper, we propose an anisotropic neural representation
learning method that utilizes learnable view-dependent features to improve
scene representation and reconstruction. We model the volumetric function as
spherical harmonic (SH)-guided anisotropic features, parameterized by
multilayer perceptrons, facilitating ambiguity elimination while preserving the
rendering efficiency. To achieve robust scene reconstruction without anisotropy
overfitting, we regularize the energy of the anisotropic features during
training. Our method is flexiable and can be plugged into NeRF-based
frameworks. Extensive experiments show that the proposed representation can
boost the rendering quality of various NeRFs and achieve state-of-the-art
rendering performance on both synthetic and real-world scenes.

通过学习隐式体积表示来改善场景表示和重建的方法，利用学习的视角相关特征将体积函数建模为球谐（SH）引导的各向异性特征，通过参数化多层感知机提高渲染质量，并通过能量正则化实现鲁棒的场景重建，该方法可在基于 NeRF 的框架中应用。广泛的实验证明了所提出表示方法对于多种 NeRF 和合成真实场景的渲染质量的提升，达到了最先进的渲染性能。