5 citations · 5 across the 6 of their papers we have counts for
4 papers · 1 filter
Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data
Haoan Feng, Xin Xu, Leila De Floriani
Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function. For terrain elevation data, this supports analytic derivatives, arbitrary-resolu…
Adaptive Training of INRs via Pruning and Densification
Diana Aldana, João Paulo Lima, Daniel Csillag +4
Encoding input coordinates with sinusoidal functions into multilayer perceptrons (MLPs) has proven effective for implicit neural representations (INRs) of low-dimensional signals,…
SASNet: Spatially-Adaptive Sinusoidal Networks for INRs
Haoan Feng, Diana Aldana, Tiago Novello +1
Sinusoidal neural networks (SIRENs) are powerful implicit neural representations (INRs) for low-dimensional signals in vision and graphics. By encoding input coordinates with sinus…
ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis
Haoan Feng, Xin Xu, Leila De Floriani
Digital terrain models (DTMs) are pivotal in remote sensing, cartography, and landscape management, requiring accurate surface representation and topological information restoratio…