Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

• URL: http://arxiv.org/abs/2607.21485v1
• Date: Thu, 23 Jul 2026 16:26:51 GMT
• Title: Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation
• Abstract summary: We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations.<n>We empirically validate the resulting spectral behavior against feed-forward INRs, non-sinusoidal recurrent variants, and equilibrium-style sinusoidal models.
• Score: 19.765812812167407
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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