  Neural radiance fields (NeRF) have demonstrated the potential of
coordinate-based neural representation (neural fields or implicit neural
representation) in neural rendering. However, using a multi-layer perceptron
(MLP) to represent a 3D scene or object requires enormous computational
resources and time. There have been recent studies on how to reduce these
computational inefficiencies by using additional data structures, such as grids
or trees. Despite the promising performance, the explicit data structure
necessitates a substantial amount of memory. In this work, we present a method
to reduce the size without compromising the advantages of having additional
data structures. In detail, we propose using the wavelet transform on
grid-based neural fields. Grid-based neural fields are for fast convergence,
and the wavelet transform, whose efficiency has been demonstrated in
high-performance standard codecs, is to improve the parameter efficiency of
grids. Furthermore, in order to achieve a higher sparsity of grid coefficients
while maintaining reconstruction quality, we present a novel trainable masking
approach. Experimental results demonstrate that non-spatial grid coefficients,
such as wavelet coefficients, are capable of attaining a higher level of
sparsity than spatial grid coefficients, resulting in a more compact
representation. With our proposed mask and compression pipeline, we achieved
state-of-the-art performance within a memory budget of 2 MB. Our code is
available at https://github.com/daniel03c1/masked_wavelet_nerf.
