Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data

ArXi:2606.00404v1 Announce Type: cross Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function. For terrain elevation data, this s analytic derivatives, arbitrary-resolution decoding, and a smooth surface model of the underlying heightfield. However, fitting and storing a separate INR for every tile does not scale to large terrain datasets. Amortized neural representations reduce this cost with a shared network: a new tile is mapped to a compact per-tile payload, and a shared decoder reconstructs the heightfield from it.