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; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 2713-2722

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.