  We present a novel method to provide efficient and highly detailed
reconstructions. Inspired by wavelets, we learn a neural field that decompose
the signal both spatially and frequency-wise. We follow the recent grid-based
paradigm for spatial decomposition, but unlike existing work, encourage
specific frequencies to be stored in each grid via Fourier features encodings.
We then apply a multi-layer perceptron with sine activations, taking these
Fourier encoded features in at appropriate layers so that higher-frequency
components are accumulated on top of lower-frequency components sequentially,
which we sum up to form the final output. We demonstrate that our method
outperforms the state of the art regarding model compactness and convergence
speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural
radiance fields. Our code is available at https://github.com/ubc-vision/NFFB.
