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.

该研究介绍了一种新的神经信号模型，用于高效地对大规模信号进行高分辨率表示，并应用于各种大规模信号表示任务，其中包含文件大小为千兆级别的图像和非常大的点云，表明它需要少于 25％的参数，33％的存储器空间和 10％的计算时间，就能达到 ACORN 等竞争技术相同的表示精度。