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Rendering novel views from captured multi-view images has made considerableprogress since the emergence of the neural radiance field. This paper aims tofurther advance the quality of view synthesis by proposing a novel approachdubbed the neural radiance feature field (NRFF). We first propose a multiscaletensor decomposition scheme to organize learnable features so as to representscenes from coarse to fine scales. We demonstrate many benefits of the proposedmultiscale representation, including more accurate scene shape and appearancereconstruction, and faster convergence compared with the single-scalerepresentation. Instead of encoding view directions to model view-dependenteffects, we further propose to encode the rendering equation in the featurespace by employing the anisotropic spherical Gaussian mixture predicted fromthe proposed multiscale representation. The proposed NRFF improvesstate-of-the-art rendering results by over 1 dB in PSNR on both the NeRF andNSVF synthetic datasets. A significant improvement has also been observed onthe real-world Tanks & Temples dataset. Code can be found athttps://github.com/imkanghan/nrff.

Multiscale Tensor Decomposition and Rendering Equation Encoding for View Synthesis