Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

Hyunmin Cho, Jaejun Yoo, Kyong Hwan Jin

Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation: 7 upvotes on Hugging Face Daily Papers, #17 of 22 papers on 2026-07-24. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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