English(EN) Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

新型循环正弦INR提升高保真图像和3D表示性能

作者 PulseAugur 编辑部 · [3 个来源] · 2026-07-23 00:00

研究人员开发了一种使用正弦循环来增强隐式神经表示（INR）的新方法。这种迭代方法丰富了潜在表示的频谱支持，从而在图像超分辨率、NeRF和符号距离函数（SDF）估计等任务中提高了性能。与现有的前馈INR相比，所提出的架构以更少的参数和优化步骤实现了更高的保真度。 AI

影响这项研究可能为各种计算机视觉和3D表示任务带来更高效、更高保真的模型。

排序理由该集群包含两篇关于隐式神经表示新方法的学术论文预印本。

AI 生成摘要 · Google Gemini · 来自 3 个来源。我们如何撰写摘要 →

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-23 16:26

用于高效高保真表示的循环正弦INR

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 …
Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-23 00:00

用于高效高保真表示的循环正弦INR

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 …
arXiv cs.CV TIER_1 English(EN) · Hyunmin Cho, Jaejun Yoo, Kyong Hwan Jin · 2026-07-24 04:00

用于高效高保真表示的循环正弦INR

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…