改进傅里叶特征嵌入，提升隐式神经表示的鲁棒性
Robustifying Fourier Features Embeddings for Implicit Neural Representations
- 用多层感知机优化傅里叶特征嵌入，缓解频谱偏差
- 在真实场景下显著降低输出噪声，提升下游任务性能
- 适合图像/3D重建等需要高精度隐式建模的研究者
隐式神经表示（INRs）通过神经网络将坐标映射为函数值，广泛应用于逆图形等领域。然而，在包含多频率成分的场景中，INRs易受频谱偏差影响。现有方法常采用基于傅里叶特征的编码（如位置编码）来缓解此问题，但会引入噪声，降低下游任务表现。本文首次提出假设：多层感知机（MLPs）与傅里叶特征嵌入相互增强，但也继承了后者固有的局限。通过一个简单定理验证该假设，进而提出无需加法操作的MLP设计策略，有效抑制噪声传播，显著提升隐式表示的鲁棒性。实验表明，该方法在NeRF、3D重建等任务中优于现有基线。
原文摘要 · Abstract (English)
Implicit Neural Representations (INRs) employ neural networks to represent continuous functions by mapping coordinates to the corresponding values of the target function, with applications e.g., inverse graphics. However, INRs face a challenge known as spectral bias when dealing with scenes containing varying frequencies. To overcome spectral bias, the most common approach is the Fourier features-based methods such as positional encoding. However, Fourier features-based methods will introduce noise to output, which degrades their performances when applied to downstream tasks. In response, this paper initially hypothesizes that combining multi-layer perceptrons (MLPs) with Fourier feature embeddings mutually enhances their strengths, yet simultaneously introduces limitations inherent in Fourier feature embeddings. By presenting a simple theorem, we validate our hypothesis, which serves as a foundation for the design of our solution. Leveraging these insights, we propose the use of multi-layer perceptrons (MLPs) without additive
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