  Implicit neural representations with multi-layer perceptrons (MLPs) have
recently gained prominence for a wide variety of tasks such as novel view
synthesis and 3D object representation and rendering. However, a significant
challenge with these representations is that both training and inference with
an MLP over a large number of input coordinates to learn and represent an
image, video, or 3D object, require large amounts of computation and incur long
processing times. In this work, we aim to accelerate inference and training of
coordinate-based MLPs for implicit neural representations by proposing a new
split MLP architecture, CoordX. With CoordX, the initial layers are split to
learn each dimension of the input coordinates separately. The intermediate
features are then fused by the last layers to generate the learned signal at
the corresponding coordinate point. This significantly reduces the amount of
computation required and leads to large speedups in training and inference,
while achieving similar accuracy as the baseline MLP. This approach thus aims
at first learning functions that are a decomposition of the original signal and
then fusing them to generate the learned signal. Our proposed architecture can
be generally used for many implicit neural representation tasks with no
additional memory overheads. We demonstrate a speedup of up to 2.92x compared
to the baseline model for image, video, and 3D shape representation and
rendering tasks.
