We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and convergence speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields. Our code is available at https://github.com/ubc-vision/NFFB.

翻译：我们提出了一种新颖方法，用于实现高效且高度细节化的重建。受小波启发，我们学习了一个能够同时在空间和频率维度分解信号的神经场。我们遵循近期基于网格的空间分解范式，但与现有工作不同，通过傅里叶特征编码鼓励每个网格存储特定频率。随后，我们应用具有正弦激活函数的多层感知机，在适当的层输入这些傅里叶编码特征，使得高频分量依次叠加在低频分量之上，并通过求和形成最终输出。我们证明，该方法在模型紧凑性和收敛速度方面优于现有技术，可应用于多项任务：二维图像拟合、三维形状重建以及神经辐射场。我们的代码已开源至https://github.com/ubc-vision/NFFB。