Anisotropic Neural Representation Learning for High-Quality Neural Rendering
This work addresses a specific bottleneck in neural rendering for computer vision applications, offering an incremental improvement to existing NeRF frameworks.
The paper tackles the problem of ambiguous features and limited reconstruction quality in neural radiance fields (NeRFs) by proposing an anisotropic neural representation learning method, which improves rendering quality and achieves state-of-the-art performance on synthetic and real-world scenes.
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.