Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data 事件

Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data arXiv:2606.00404v1 Announce Type: new Abstract: Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function. For terrain elevation data, this supports 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