  We introduce a new neural signal model designed for efficient high-resolution
representation of large-scale signals. The key innovation in our multiscale
implicit neural representation (MINER) is an internal representation via a
Laplacian pyramid, which provides a sparse multiscale decomposition of the
signal that captures orthogonal parts of the signal across scales. We leverage
the advantages of the Laplacian pyramid by representing small disjoint patches
of the pyramid at each scale with a small MLP. This enables the capacity of the
network to adaptively increase from coarse to fine scales, and only represent
parts of the signal with strong signal energy. The parameters of each MLP are
optimized from coarse-to-fine scale which results in faster approximations at
coarser scales, thereby ultimately an extremely fast training process. We apply
MINER to a range of large-scale signal representation tasks, including
gigapixel images and very large point clouds, and demonstrate that it requires
fewer than 25% of the parameters, 33% of the memory footprint, and 10% of the
computation time of competing techniques such as ACORN to reach the same
representation accuracy.
