  Neural radiance fields (NeRFs) have shown impressive results for novel view
synthesis. However, they depend on the repetitive use of a single-input
single-output multilayer perceptron (SISO MLP) that maps 3D coordinates and
view direction to the color and volume density in a sample-wise manner, which
slows the rendering. We propose a multi-input multi-output NeRF (MIMO-NeRF)
that reduces the number of MLPs running by replacing the SISO MLP with a MIMO
MLP and conducting mappings in a group-wise manner. One notable challenge with
this approach is that the color and volume density of each point can differ
according to a choice of input coordinates in a group, which can lead to some
notable ambiguity. We also propose a self-supervised learning method that
regularizes the MIMO MLP with multiple fast reformulated MLPs to alleviate this
ambiguity without using pretrained models. The results of a comprehensive
experimental evaluation including comparative and ablation studies are
presented to show that MIMO-NeRF obtains a good trade-off between speed and
quality with a reasonable training time. We then demonstrate that MIMO-NeRF is
compatible with and complementary to previous advancements in NeRFs by applying
it to two representative fast NeRFs, i.e., a NeRF with sample reduction
(DONeRF) and a NeRF with alternative representations (TensoRF).
