FINER: Flexible spectral-bias tuning in Implicit NEural Representation
  by Variable-periodic Activation Functions
Zhen Liu Hao Zhu Qi Zhang Jingde Fu Weibing Deng Zhan Ma Yanwen Guo Xun Cao
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
restricted capability to tune their supported frequency set, resulting in
imperfect performance when representing complex signals with multiple
frequencies. We have identified that this frequency-related problem can be
greatly alleviated by introducing variable-periodic 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
variable-periodic function are selected for activation. Consequently, the
supported frequency set of FINER can be flexibly tuned, leading to improved
performance in signal representation. We demonstrate the capabilities of FINER
in the contexts of 2D image fitting, 3D signed distance field representation,
and 5D neural radiance fields optimization, and we show that it outperforms
existing INRs.