SASNet: Spatially-Adaptive Sinusoidal Networks for INRs
SIRENs are powerful implicit neural representations, but they are highly sensitive to frequency initialization: low frequencies over-smooth fine detail, while high frequencies inject noise into smooth regions. SASNet addresses this with a fixed frequency embedding paired with jointly learned spatially-adaptive masks that localize each sinusoidal neuron’s influence across the domain. The result is sharper reconstructions, less noise, and faster convergence across image fitting, 3D volumes, and SDF reconstruction — consistently outperforming INR baselines.
Jun 1, 2026
Structured Pruning in Implicit Neural Representations
We are actively working on the formating of this paper.
Sep 15, 2024