Rendering novel views from captured multi-view images has made considerable progress since the emergence of the neural radiance field. This paper aims to further advance the quality of view rendering by proposing a novel approach dubbed the neural radiance feature field (NRFF) which represents scenes in the feature space. We first propose a multiscale tensor decomposition scheme to organize learnable features so as to represent scenes from coarse to fine scales. We demonstrate many benefits of the proposed multiscale representation, including more accurate scene shape and appearance reconstruction, and faster convergence compared with the single-scale representation. Instead of encoding view directions to model view-dependent effects, we further propose to encode the rendering equation in the feature space by employing the anisotropic spherical Gaussian mixture predicted from the proposed multiscale representation. The proposed NRFF improves state-of-the-art rendering results by over 1 dB in PSNR on both the NeRF and NSVF synthetic datasets. A significant improvement has also been observed on the real-world Tanks and Temples dataset.

翻译：从捕获的多视图图像渲染新视图，自神经辐射场出现以来已取得显著进展。本文旨在通过提出一种名为神经辐射特征场（NRFF）的新方法进一步提升视图渲染质量，该方法在特征空间中表示场景。我们首先提出一种多尺度张量分解方案，用于组织可学习特征，从而从粗粒度到细粒度表示场景。我们展示了所提多尺度表示的诸多优势，包括比单尺度表示更精确的场景形状和外观重建以及更快的收敛速度。不同于编码视角方向以建模视角相关效应，我们进一步提出通过利用所提多尺度表示预测的各向异性球形高斯混合，在特征空间中编码渲染方程。所提NRFF在NeRF和NSVF合成数据集上，将最先进的渲染结果的峰值信噪比（PSNR）提升了超过1 dB。在真实世界的Tanks and Temples数据集上也观察到了显著改进。