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New Recurrent Sinusoidal INRs Boost High-Fidelity Image and 3D Representation

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new method using sinusoidal recurrence to enhance implicit neural representations (INRs). This iterative approach enriches the spectral support of latent representations, leading to improved performance in tasks like image super-resolution, NeRF, and Signed Directional Distance Function (SDF) estimations. The proposed architecture achieves higher fidelity with fewer parameters and optimization steps compared to existing feed-forward INRs. AI

IMPACT This research could lead to more efficient and higher-fidelity models for various computer vision and 3D representation tasks.

RANK_REASON The cluster contains two academic paper preprints detailing a new method for implicit neural representations.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New Recurrent Sinusoidal INRs Boost High-Fidelity Image and 3D Representation

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The cluster contains two academic paper preprints detailing a new method for implicit neural representations.
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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

    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 …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

    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 …

  3. arXiv cs.CV TIER_1 English(EN) · Hyunmin Cho, Jaejun Yoo, Kyong Hwan Jin ·

    Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

    arXiv:2607.21485v1 Announce Type: new 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 spect…

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