6 papers
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…
ImplicitTerrainV2: Wavelet-Guided Spatially Adaptive Neural Terrain Representation
Haoan Feng, Xin Xu, Leila De Floriani
Digital elevation models (DEMs) underpin terrain analysis in Geographic Information Systems (GIS), but commonly as raster representation, they rely on interpolation for off-grid sa…
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…
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,…
Critical Features Tracking on Triangulated Irregular Networks by a Scale-Space Method
Haoan Feng, Yunting Song, Leila De Floriani
The scale-space method is a well-established framework that constructs a hierarchical representation of an input signal and facilitates coarse-to-fine visual reasoning. Considering…
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…