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
@article{arxiv.2502.05482,
title = {Robustifying Fourier Features Embeddings for Implicit Neural Representations},
author = {Mingze Ma and Qingtian Zhu and Yifan Zhan and Zhengwei Yin and Hongjun Wang and Yinqiang Zheng},
journal= {arXiv preprint arXiv:2502.05482},
year = {2025}
}