Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation
Hyunmin Cho, Jaejun Yoo, Kyong Hwan Jin
This paper develops a new architecture for implicit neural representations (INRs) that uses sinusoidal recurrence to improve the quality of the representations, and it demonstrates that this approach can lead to better image and 3D representations with fewer parameters and less computation.
Abstract
We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveals that sinusoidal activations induce a harmonic line spectrum, providing a spectral account of how recurrent unrolling enriches the effective spectral support. We realize this principle with a shared sinusoidal block that iteratively refines the latent representation. We empirically validate the resulting spectral behavior against feed-forward INRs, non-sinusoidal recurrent variants, and equilibrium-style sinusoidal models. Complementing this analysis, we evaluate the proposed architecture across image and 3D representation tasks. On RGB image benchmarks, our method achieves higher fidelity than feed-forward baselines with fewer parameters and fewer optimization steps, and it further transfers favorably to super-resolution, NeRF, and SDF tasks.