INR

A controlled benchmark of amortized neural representations on high-resolution (1 m/pixel) terrain elevation data, and HUVR+SIREN — a hypernetwork with a smooth, analytically differentiable decoder that attains the best height and derivative fidelity with no extra per-tile storage.

Nov 3, 2026

A wavelet-guided, spatially adaptive implicit neural representation for digital elevation models. A wavelet complexity field localizes high-frequency capacity to complex terrain, and post-training compression reaches 1.23 bpp — 66.25 dB PSNR on Swiss terrain tiles, +5.70 dB over prior work with 3.2x fewer parameters.

Nov 3, 2026

We present our CVPR 2026 paper "SASNet: Spatially-Adaptive Sinusoidal Networks for INRs" at the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Jun 3, 2026

A spatially-adaptive sinusoidal network for implicit neural representations. A frozen frequency embedding fixes the frequency support while jointly learned spatial masks localize each neuron, giving sharper edges, less background noise, and faster convergence on images, volumes, and SDFs.

Jun 1, 2026

Work in progress on structured pruning for implicit neural representations — removing whole sinusoidal neurons from a trained INR to shrink the model while preserving reconstruction quality.

Sep 15, 2024

We present our CVPR 2024 paper "ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis" at the 1st Workshop on Implicit Neural Representation for Vision

Jun 18, 2024

ImplicitTerrain models high-resolution digital terrain continuously and differentiably with an implicit neural representation, enabling accurate surface fitting and parallel topological feature extraction directly from the compact learned model.

Jun 18, 2024