FINER: Flexible Spectral-Bias Tuning in Implicit NEural Representation by Variableperiodic Activation Functions
Zhen Liu, Hao Zhu, Qi Zhang, Jingde Fu, Wei Deng, Zhan Ma, Yanwen Guo, Xun Cao
Abstract
Implicit Neural Representation (INR), which utilizes a neural network to map coordinate inputs to corresponding attributes, is causing a revolution in the field of signal processing. However, current INR techniques suffer from a re-stricted capability to tune their supported frequency set, re-sulting in imperfect performance when representing complex signals with multiple frequencies. We have identified that this frequency-related problem can be greatly alleviated by introducing variableperiodic activation functions, for which we propose FINER. By initializing the bias of the neural network within different ranges, sub-functions with various frequencies in the variableperiodic function are selected for activation. Consequently, the supported frequency set of FINER can be flexibly tuned, leading to improved performance in signal representation. We demon-strate the capabilities of FINER in the contexts of2D image fitting, 3D signed distance field representation, and 5D neural radiance fields optimization, and we show that it outper-forms existing INRs.