  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.
